Cell Assembly Memetics

Brain software can be understood in terms of a hyperdimensional computing framework, creating software-level entities which are made from neuronal activation and replicate across neuron timesteps. These are memes [Dawkins, Dennett, Blackmore, Deutsch] of neuroscience. (see The Biology of Cell Assemblies / A New Kind of Biology)

This cell assembly memetics is software but understood in terms of a kind of biology. The cell assemblies are living entities, which need to have strategies for surviving, competing, and harmonizing. They have a structure and function of their own, which I call their 'ad-hoc epistemology'.

A network of neuronal units has meaning by being connected to sensors and motors [Braitenberg 1984, cybernetics, Stafford Beer The purpose of a system is what it does (POSIWID)]. If a subnetwork is connected in a vast hyperdimensional derived point in meaning space, then this is what it means.

Brain forms groups of connected neurons, or microcircuits [Schütz and Braitenberg, 2001; Shepherd, 2004]. They are also called ensembles, assemblies, cell assemblies or attractors and exhibit synchronous or correlated activity [Lorente de Nó 1938, Hebb 1949, Hopfiled 1982, Abeles 1991, Yuste 2015].

These cell assemblies form spontaneously, they emerge out of simple 'Hebbian substrate' models. They are a high-dimensional representation of the inputs, and can be seen as data structures in a computing framework, where the fundamental operation is pattern complete [Dabagia, Papadimitriou, Vempala 2022]². They are self-activating pieces of the subnetwork.

Cell assemblies can be connected in just the right way to the network to mean things corresponding to actual models of the world. To see this, imagine a few alternative sub-networks. Some will stay active, even though the sensors change. They will represent causal entities in the world.

Braitenberg musings The synaptic structure of the nerve net will approximate the causal structure of the environment:

Macrocosm and microcosm. Insufficient as this picture of the cortex may be, it is close to a philosophical paradigm, that of the order of the external world mirrored in the internal structure of the individual. If synapses are established between neurons as a consequence of their synchronous activation, the correlations between the external events represented by these neurons will be translated into correlations of their activity that will persist independently of further experience. The synaptic structure of the nerve net will approximate the causal structure of the environment. The image of the world in our brain is an expression that should not be understood in an all too pictorial, geometric sense. True, there are many regions in the brain where the coordinates of the nerve tissue directly represent the coordinates of some sensory space, as in the primary visual area of the cortex or the acoustic centers. But in many other instances, we should rather think of the image in the brain as a graph representing the transition probabilities between situations mirrored in the synaptic relations between neurons. Also, it is not my environment that is photographed in my brain, but the environment plus myself, with all my actions and perceptions smoothly integrated in my internal representation of the world. I can experience how fluid the border between myself and my environment is when I scratch the surface of a stone with a stick and localize the sensation of roughness in the tip of the stick, or when I have to localize my consciousness in the rear of my car in order to back it into a narrow parking space.

(The Common Sensorium: An Essay on the Cerebral Cortex, 1977).

From simple biological reasoning (to prevent epilepsy), this activity needs to be kept in check, presumably by some area-wise (or global) inhibition. (For instance by only allowing a fixed amount of neurons to be active at each time step). This allows for parallel search mechanisms, which will find the best-connected subnetwork, given the context. [Valentino Braitenberg 1977, Guenther Palm 1982]. (Also called threshold device, inhibition model, a hypothetical oscillatory scheme of this is called though-pump; See Tübinger Cell Assemblies, The Concept Of Good Ideas And Thought Pumps).

This inevitably leads to the view that this activation can survive, i.e. replicate across neuron timesteps. Hence, the cell assemblies can be analyzed in terms of abstract replicator theory [Darwin, Dawkins]. The simplest meme is activate everybody, epilepsy is a memetic problem. Note the simplest pessimistic meme is activate nobody. This meme will immediately die out and not get anywhere.

How does activation have strategies? By being connected in the right way to the rest of the network. By being connected in just the right way that is supported by the current interpretation of the network. Cell assemblies complete for 'well-connectedness'. In a network where the connections have meaning.

This yields a fundamental, high-level understanding of neuroscience, its activation flows and circuitry; Unifying brain-software with abstract replicator theory and hence biology. The purpose of activation flow is to replicate itself. It needs good strategies (free-floating rationals) [Dennett], in order to be stable across time. Being on is the only thing the memes care about, fundamentally.

The brain creates the substrate to support a second biological plane, the cell assemblies; With their software-logic biology. They will spread whatever neuronal tissue there is available. They will try to be stable and bias the system to be active again. For instance, they will simply spread into a flash drive neuronal tissue, when available. There is only one thing you need memes want to be active. If they can inhibit their competitors, they will do so. If they can activate their activators, they will do so. They don't care about some other neuronal tissue, they only care because their competitors might find excitation support from that other tissue. If they can make the system stop thinking, they will do so. They will lay association lines towards themselves if they can do so. More speculative ideas on some circuits: (A Curious Arrangement).

Whatever cell assemblies are active can contribute to cognition, the rest of the network is important only because it supports all possible activation. All activation is meaningful because it flows into motors ultimately. Humans with their explanation seem to challenge this idea at first glance, until you realize that even a person coming up with a really interesting internal state of ideas in a deprivation tank, must come out of that tank eventually to share their idea. Alternatively, we build new kinds of sensors and BCIs, which make it possible to broadcast one's ideas out of one's internal states. But then we have arguably crafted a new kind of effector.

We can expect that the Darwinistically grown part of the brain implements mechanisms that shape the memetic landscapes, dynamically so perhaps. The wiring of the brain must create selection pressures for memes that make them about being an animal in a world; Otherwise, a brain would not have been useful evolutionarily. There is no guarantee that this arrangement will work, and we can observe many failure modes; Superstition, false-believes, and so forth. What we can say is that the memes we see around are the memes that play the game of circuits well. Therefore the structure and function of the cell assemblies will be their ad-hoc epistemology; Their connectivity is the knowledge they contain, about how to replicate in the network. If the network is biased in the right ways, this knowledge will represent knowledge about the world and being an animal in it.

For instance, a cell assembly that is on for all visuals of bananas and the smell of bananas and so forth has a wonderful strategy. It is connected in the right way to banana things so that it is on for all bananas, it is allowed to represent the abstract notion of bananas. Not losing sight of the fact that this is biological (hence messy), that the ideas need to grow and so forth; Truly abstract, timeless concepts can only ever be represented as approximations.

This banana meme is a competitor in the game of the circuits. If it can bias the system to think of bananas more, it will do so. If it can bias the system to make explanations and stories, using bananas as an analogy, it will do so. Memes want to be implicated in as many interpretations as possible.

The cell assemblies have their own replication rules; For instance, they will merge (associate) with other memes, leaving their identity behind.

The interpretation game

Cell assemblies can be 'Situations', 'templates', 'schemata', 'expectations', and 'queries', providing context. The software does 'interpretation', 'pattern complete', 'filling the blanks', 'results', 'autocomplete' or 'expectation fulfilling'. 'Confabulation' and expectation are the fundamental operations in this software framework, then.

The means of abstraction [Sussman, Abelson 1984] in this software paradigm are 'situations'. Allowed to be small or big, stretch across time and so forth.

Believes and explanation structures can be represented as 'expectations', shaping which memes are active across sensor and meaning levels. Memes can be said to participate in the interpretation game, a series of best guesses of expectations, or explanation structures. For instance, the belief I see a blue-black dress, is allowed to be instantiated in the expectation, of seeing a blue dress.

If there is a cell assembly that activates blue and is activated by blue in turn (it is the same cell assembly, stretching across meaning levels), if it is stabilizing itself by inhibiting its alternatives (see Contrast And Alternative). If it also makes the system stop moving forward in its interpretation, i.e. it makes the system fall into an attractor state, makes it stuck in the interpretation 'I see a blue dress'. We can say 'the system believes that there is a blue dress'.

Cell assemblies are allowed to be temporarily allocated sub-programs, which are self-stabilizing and represent an interpretation. From the anatomy of the cortex, we see that motor areas, sensor areas, and 'association' areas are all wired in parallel (see Input Circuits And Latent Spaces / The Games of the Circuits), so what I call 'higher-meaning-level' really are equal participants in an ongoing 'situation analysis'.

In a joyful twist of reasoning, perception is created by the stuff that we don't see. Just as science is about the explanation structures that we don't see [Popper, Deutsch]. Perception is a set of expectations, each being supported by and supporting in turn sensor-level interpretations. Note that this situation analysis always includes the animal itself, in the world.

In order to get output you might say the system is predicting itself in the world, including its movement. Although below I label a similar concept commitment instead. (Speculations On Striatum, Behaviour Streams).

Memes want to be active. From this, we see there are memetic drivers for generality and abstraction, a meme that is on in many situations is good.

Children will overgeneralize the rules of language. Analogies are allowed to grow. Memes with pronunciation (words) will want to be pronounced more. Further, there are higher-order memes that will look for these general memes and have agendas for such memes to be general. (see Cell Assemblies Have Drivers For Generality and Abstraction). This is because a meme that is associated with successful memes is active more.

Some memes want to be good building blocks, they want to fit many situations, and they will be symbiotic with other such memes that 'fit well together'. For instance, the memes represent the physical world like objects, their surfaces, their weight, whether something can be put into them, whether they can be balanced, whether they melt in the sun and so forth are symbiotically selected by being part of larger memeplexes, representing the explanation structure of the physical world, I call such an ad-hoc abstract language of the physical world common sense. The best memes will play the interpretation game by composing abstract building block memes in elegant ways.

This is software engineering reasoning, we will see that abstract memes, and memes that are composable with other memes (memes that make good languages together), will be useful software entities.

(The memes will play the game of the circuits in order to reproduce. If the circuit is laid out in clever ways, a meme might be forced to play a different kind of game. See Input Circuits And Latent Spaces / The Games of the Circuits for some ideas on thalamocortical circuitry and what kinds of memes it produces).

Assuming a memetic landscape with bias on navigating an animal in the world;

Memes have drivers for using the computer they run on, without understanding the computer they run on for speed. Why speed? Because the first meme which makes the meme-machine stop thinking (Contrast And Alternative for a candidate mechanism), is good.

Memes might be said to participate in the interpretation game. That is, whatever meme is putting the brain into a stable interpretation simply wins (also called attractor states).

From top-down reasoning, a meme engine that creates a virtual simulated world and runs a high-dimensional computing framework should create user-level entities, which use magical interfaces to the rest of the computer. Why this is a mechanism to build magic interfaces, see Memetic Engines Create Competence Hierarchies Up To User Illusions.

In brief:

Consider the alternative, a meme that uses the computer clumsily is discarded. Similarly, a computer-level meme which is hard to use, is discarded. The overlap of optimism and confidence yields magic interfaces. Where the user-level entities are allowed to produce half-confabulated ideas, which are filled by the rest of the meme engine.

1. Make a simulated world (one of the fundamental goals of this software).
2. Try out everything a little bit (high dimensions and parallelism make this easy) all the time.
3. Reward information flow which somehow has to do with navigating the world as an animal. I.e. using the motors smartly and having smart ideas about what the sensors mean.

4. and 2. are a natural selection algorithm. First, all kinds of meanings are possible a little bit. In the second step, the meanings that were useful is left over (I.e. subnetworks that were connected in just the right way to mean something, for instance how to use the computer, for instance how to retrieve stuff from midterm memory, or how to pay attention to something).

   Note that this algorithm can only do what the computer can do. If the flash drive module of the computer is gone, this algorithm will not develop mid-term memory for instance. Consequently, user-level entities cannot dream themselves into great powers, they are constrained by what the computer can pull off.

5. You will select high-meaning-level software entities, which are competent, fast and confident. They want to be wizards, they want to use the computer without knowing how the computer works.
6. You will select low-level software entities, which are abstract, general and harmonious (a kind of building blocks, a kind of language). They want to be magical. Then they can be used by many other memes. Then they can be on.

I think there is a reason why we speak of ideas sometimes in biological terms the seed has been planted, the idea is budding. It is because the ideas are biological entities, they are replicators.

Brain software properties:

• The computer we run on is fast (parallel) (something like 100ms to recognize a new object)

  Cell assemblies can find interpretations within a few neuron timesteps. This stuff is fast and parallel. Every theory of what neurons do needs to address this parallelism in my opinion (or not be a better idea than cell assemblies).

  Thought pump mechanisms can make a global, parallel search. Finding the best interpretation available to the system.

  Assembly calculus makes multi-sensory integration trivial. The same arrangement will represent the combination of sensor inputs just as well. (Given a neuronal area with multi-sensor inputs).

• Brain software is used seamlessly (literally feels like magic).

  (Memetic Engines Create Competence Hierarchies Up To User Illusions)

• Brain software supports feelings, hunches, and intuitions

  Cell Assemblies happily represent 'vague' information states, pointing in a general direction without details.

  The fundamental computation of cell assembly memetics is 'situation interpretation'. If there is a situation analysis, which stretches across a large situation, and is vague in some way, that looks like a hunch or intuition to me.

  What kind of circuitry is needed to make a 'long scale' situation analysis? Open questions.

  One piece of the puzzle will be the hippocampus, for sure: (See The Slow Place: Evolutionary Drivers For Mid-Term Memory).

  My current idea is that whatever the medial temporal lobe is doing, it seems to be part of grounding us as animals in the world. I.e. feelings, hunches, long-scale situation analysis, possibly credit assignment.

• The stuff of ideas is infinitely malleable. It can be put together in vast amounts of ways.

  This is supported by a high-dimensional, dynamic computing framework. The leftover question is how to grow the knowledge inside such a framework.

• A single piece of explanation can change the mind of a person forever. Like natural selection does it to biologists.

  The way that the Gene's eye view and the Extended Phenotype of Dawkins did it for me. Also called Socratic Caves. Whatever piece of explanation one has seen, it cannot be unseen. I.e. you don't go back to a cave.

  I don't know yet what brain-software is needed to support this.

• A single instance of a piece of knowledge is sufficient to be used by the brain software in the future

  A mid-term memory is necessary for this to work [see cognitive neuroscience on HM.].

  Cell assemblies from after very few neuron timesteps [Vempala]. If the brain keeps some information states alive for a while, it can represent its inputs to itself, and form cell assemblies.

  In general, memes will want to spread into a flash drive (mid-term memory), if available.

  Details of possible and useful flash drives would be a fruitful topic of cell assembly memetics.

• Children over-generalize language rules when they acquire language

  Memes have drivers for abstraction and generality.

• Brain software can represent counterfactuals, hypothetical and imagination

• Brain software can take on different perspectives, which can immediately flip the interpretation globally

  For instance when walking down a street in a new city and suddenly realizing one was walking in a different direction than one was thinking.

• Brain software seamlessly switches between high-resolution details and low-resolution global views

  For instance when remembering a trip. Hofstadter paints the picture of seeing first the mountain tops of memory, the highlights and global views, then zooming in and lifting the fog in the valleys. 'Like this day of the trip…'. Now the perspective switches and more detailed memories come to mind.

• Brian software creates a virtual simulated world, which supports decision-making and explanation-making software entities

You need to circle in from the big ideas into the small ideas, that way you know nothing is left out. (Just getting this out of the way, feel free to skim to below).

1. The brain evolved as an animal-navigation device, in a broad sense of the term.
2. The basic building plan is the one from Vehicle 1:

 world----------+
   ^            v
   |         sensors
   |           +
   |           |
   |           |
   |           [B]---------- 'wiring
   |           |             (meaning)
   |           |
   |           |
   | (body)    +
   +--------actuators



                       'wiring' (meaning)
    +---> sensors --------[B]--------------- actuators
    |                                            |
    |                                            |
    |                                            |
+---+----+---------+                             |
|   |    |  body   | world  <--------------------+
+--------+---------+
           (body is part of the world)

Note that it is perfectly fine for the wiring to go in both directions and so forth. The state of the actuators is again a kind of sensory input to the system etc. Also, the actuators change the world and the sensors perceive this change.

The wiring is allowed to become more complicated, by adding more elements, called [B] for the brain. From this we see the evolutionary driver of this system, to evolve more useful meanings.

Consider Vehicle 1 (Braitenberg 1984):

 ( )     temperature sensor
+-+-+     +
| | |     |
| | |     | connection
+-+-+     v
 [ ]     motor

This is a bacteria-like intelligence (incidentally insulting bacteria intelligence, which is made from thousands of such elements).

If the sensor is on, the animal moves, or vice versa. Depending on the sign and connectedness of the wire - it's meaning.

The wire can mean either: 'I move in hot water' or 'I move unless there is hot water'. (By negating the excitation effect of the sensor to the motor).

This allows us to draw a 2x2 matrix, imagine the case where hot water is detrimental to the animal in its environment:

  move!      stay!    (the meaning of B for the animal given a world, hot temperature)

+---------+--------+
|   S     |   X    | competent (sensors and motors work)
|         |        |
+---------+--------+
|   X     |   X    | incompetent
|         |   S    | (luck)
+---------+--------+

X means you die and are discarded by evolution, but you are allowed to be tried out. S means 'success' so these are the animals that we see running around [Darwin 1859].

Whatever complexity B is growing into then, Its fundamental evolutionary driver is 'success'.³

Here is a challenge to computational models of cognition⁴, what is this program and software then, that runs on the brain? As a computational cybernetician, I need to think about the thoughts that say 'Those are the kinds of programs that are cognition machines'. I need strong ideas about the nature of this program.⁵ I cannot be content with the general direction of the idea, I need a building plan or a development plan.

One perspective I have is that the brain implements a 'success machine'.⁶ We get an evolutionary driver for `abstraction`. Because I need abstract ideas in a resource-constrained world in order to have success (short and meaningful memes). From this, the computer programmers' intuition about the nature of abstraction and the power of programming languages starts forming a brain-software-shaped question mark, waiting to be filled with ideas of what kind of building blocks, computational primitives and organization rules could exist that would shape the memetic landscapes of an idea-machine.⁷

I believe you should be able to point to the algorithm/software/program in its infancy and have a story of how that evolved.

I call this move 'putting the algorithms into the world', to realize that the programs are evolved entities, too.⁸ A brain must be fundamentally optimistic⁹, we can consider a pessimistic brain 'if I am in hot water, I will stay in hot water'. It will be discarded by evolution. It is only the brain that says 'If I am in hot water, I will not stay in hot water' that can have success.

Alternative names: 'Evolution favors competence', 'The survival algorithm' (G. Palm), 'Meaning evolves', 'Purpose', 'Purpose-Engine', 'optimism-drive', 'abstraction-driver', 'abstract memes are useful', and 'the space between your sensors and motor needs to be short'.

—

I will keep coming back to this holy overlap of competence and optism. Turns out that is a deep principle of memetics, too.

One of the important things you need to do as a software developer is to keep the overall picture in mind. What does the system need to accomplish? It is like dreaming up dream castles floating in the air, not because they are cool, but because this is the first of a 2-step process.

In the second step, we wonder what foundation we need to support these kinds of castles. It is a constant back and forth, between the kinds of stuff the system needs to be in the first place, and the kinds of stuff we can build with the materials at hand. Between sup-parts of the existing ideas, in juxtaposition with a potential new idea. The most beautiful thing about programming is that part of what we do is coming up with new kinds of building materials. Different names for the same thing are 'languages', 'tools', or 'building blocks'; each with a different emphasis.

These are sort of the main overarching principles I use to circle in on models of cognition. The software development approach.

Think of what kinds of building material the brain might be making, then think about what kinds of problems the system needs to solve, and then think about what kinds of mechanisms can use the building materials at hand. Then realizing there are more requirements for the kind of stuff we need to express and so forth.

—

Imagination is the only way we can hope to build models about the world. I think that explaining cognition will be similar to explaining life and natural selection. I want my explanations to click, like beautiful toys and colorful candy. It is the spirit of the Braitenberg Vehicles (overview).

This is an exercise in fictional science, or science fiction, if you like that better. Not for amusement: science fiction in the service of science. Or just science, if you agree that fiction is part of it, always was, and always will be as long as our brains are only minuscule fragments of the universe, much too small to hold all the facts of the world but not too idle to speculate about them.

Braitenberg

The words of the language, as they are written or spoken, do not seem to play any role in my mechanism of thought

Albert Einstein

Elegance?

Pardon me, Your Honor, the concept is not easy to explain – there is an ineffable quality to some technology, described by its creators as a concinnitous, or technically sweet, or a nice hack – signs that it was made with great care by one who was not merely motivated but inspired. It is the difference between an engineer and a hacker.

Judge Fang and Miss Pao in Neal Stephenson's The Diamond Age, or, A Young Lady's Illustrated Primer

1. Church-Turing Completeness means first that all universal computers can compute everything computable.
2. From McCulloch and Pitts [1943] we can assume that neurons are in principle universal.
3. 'Universal' sounds grand, but the inverse insight is that we have an upper bound for their power, per theorem.

4. Computer programming is not concerned with the (universality), absolute power of a computing system. It is concerned with what is idiomatic, what abstractions exist, what languages exist, that building blocks exist, it is concerned with the kinds of verbs a system provides.

   -> A central question of comparing computational systems, including brain-software is

   What is easy to say?

5. Another way to see this is to consider that you could recursively express all possible lisp programs in a few lines of code. Or imagine you write assembly, you can always add the next instruction at the bottom. A program that generates all possible programs, including brain-software, is easy. This is sometimes expressed by saying All problems in AI and neuroscience are optimization problems¹³
6. I think viewing a computational system from the lens of optimization is like viewing sex from the lens of health benefits. The alternative has to do with composition, harmony, good design - sensuality. A cognitive machine is efficient when it provides powerful abstractions to itself, just like good software is good because it is designed with care.
7. Consider how programming languages and paradigms have different flair, and different strengths, and make different things easy to say.¹⁴
8. When we say the primitives of the system, this is not derogatory. To the contrary. The so-called "primitives" are the fundamental building blocks that the designer of the system came up with. If it is a well-designed system, then the programmer can express what they want to say straightforwardly, in terms of the primitives of the system. This is the same concept as having nice building blocks, out of which something larger can be built. It is the harmony, the way the building blocks fit together, that is the property of a well-designed system. In other words, elegant systems are expressed in terms of their primitives, with straightforward swag, also called elegance. And the most powerful programs are self-evident¹⁵.

This is the meaning of "finding the building materials of cognition". Finding a computational system, a language, the fundamental data structures, or the verbs of the right kind - in terms of which cognition can build itself.¹⁶

—

When we say the computational properties of a system, or data structure. The meaning is what kinds of things does this make easy to say. That is on the plane of elegance, design, and software engineering. Not absolute power. And only incidentally has to do with its speed.

One view then, the view from the bottom, is:

The structure and function of the cortex and its nuclei is the kind of computation it implements.

The kind of computation the brain implements, I submit, is the layer in between us, the neurons and a model of cognition.

What you are asking for is "What is the computational notation that is relevant for neuroscience?"

What is the appropriate computational notation for neuroscience? The machine code - may be neural nets but what is the higher description? It might be mathematical, that might be the case. It might be like general relativity, which is mathematical, and very manifold-based. It might be something that is more like a computational language design problem. That's the thing I would like to be able to solve.

Steven Wolfram with Tery Sejnowski

—

Note that it does not say the algorithm, the output of this system is completely open-ended. The unended malleability of the system is one of its essential properties.

[evolving notes]

As far as I know, Dan Dennett was the only public thinker talking about how the mind creates a user interface. And it interfaces down, too. The user interface is not merely an analogy, it is a perspective the fundamental nature of brain-software.

This view can be summarized in the idea that brain-software must create a kind of operating system, (which I call cognition machine), this operating system is capable of fulfilling the functional requirements of brain - making explanations, thinking, behaving and so forth, by creating user-level software entities that use the operating system. The nature of the operating system, the user level software entities, and what use means are some guiding questions for explaining brain-software.

Computational systems, from the view of a software developer, are not concerned with absolute power. Universality is precisely the notion that we don't need to worry about absolute power.

There is a wrong idea in software development currently that concludes that this means all programming languages are equally powerful. (see Paul Graham, the Blub paradox, Beating The Averages (for the origin of the term)). It cannot possibly be so, because programming languages are exactly the thing we build on top of universal computers. They are a system of expression that allows us to get more done with less effort, and this layer is somehow above the absolute power of universality. Arguably just as mathematics is said to progress with its notations and systems of thinking, programming languages and computational paradigms allow us to think different kinds of thoughts. And hence more or less powerful thoughts. What this power is, is one of the deep questions to answer. It is something that has to do with the so-called elegance of a system. This additional power beyond universality is the purpose of programming languages. Any challenge to this view can easily dismissed by the observation that programmers do not program in Assembler.

David Deutsch sometimes says AGI is the problem of software, not hardware. I.e. not a problem of absolute power, nor speed or memory. It is a problem of modes of creating explanations ¹⁷.

Since the brain is a universal [Turing, Church, Deutsch, McCulloch], classical computer [e.g. Braitenberg 1986, (see Pyramidal cell activity - The Gasoline)], it is this power, the software power, the one that makes programing languages useful that is the difference of brain-software and more primitive technology.

The structure and interpretation of computer programs, that is the analysis and understanding of what is easy to say; The space of language and interface design, is how we compare computational systems.

My answer to Steve Wolfram's question above is that the relevant computational notation for neuroscience must be a computational language design problem. We probably have to take a very broad perspective on computational language design in order to explain biological, self-assembled intelligence.

This was an intro to the analysis of what are computational interfaces?.

An attempt at defining user interface:

A user interface is a set of affordances, that constitute a kind of language, allowing the user, also called consumer of the interface to observe, transform, guide, modify, experiment with, or operate the computational state or resources of producer or provider of the interface.

This notion unifies programming languages, operating systems, mental affordances of brain-software, observability tools, shell prompts, peripherals like mice and keyboards and so forth.

We observe that computational interfaces are allowed to arbitrarily powerful, if the set of supported intents, also called the contract, includes a turing complete programming language. That is the programmer interacting with their computer operating system and code editor form an arbitrarily powerful system. This is currently not true of consumer user interfaces, which are thereby absolutely impoverished.

The notion of an interface necessarily devides the world in two. It is this splitting that allows crafting separate functional units. The same notion, but vertical, is called an 'abstraction barrier' ¹⁸, in 'stratified design' ¹⁹, we try to create a toolbox of explanation in one layer, the domain layer. Which is subsequently used by the higher layer, which might be the application layer. For instance, the basic laws of Newtonian physics can be seen as as system of explanation, with the primitives mass, direction, length, time (or something like that). In order to model a physics problem, we implement a description of this this abstract, primitive layer and then express our problem in terms of the lower layer.

Somehow, splitting a problem into such abstraction layers, where a general layer is describing the kinds of problems²⁰ we want to express. And subsequent layers use the lower layers in order to express the problem at hand is powerful.

This is also called the problem domain, which is described in a domain model or domain layer and the application layer. Note here the relationship of domain model and application. It is analogous to the relationship between user and the user interface.

In stratified design, as the name suggests, we are creating multiple of these layers on top of each other, like layers in a cake.

If part of the computer program is reaching too far below, skipping a layer of abstraction, this is called a 'level-violation' and a sign that the system is not designed well. Similarly, if an abstraction layer is violating the contract of the interface, usually because there is more going on than expected, this is called a leaky abstraction.

Level violations and leaky abstractions point out to us that the ideal interface is powerful by allowing the subcomponents to talk in terms of contracts. This is reified in the following slogan:

I don't know and I don't want to know.

Rich Hickey

The meaning of this is, we can think of a component in isolation. If components need to know about the internals of other components, that is bad.

[…]

The philosophy of kinds of doing computing style and user interfaces has not made much progress since the 60s. If one looks at the history of computer user interfaces, one will find that Alan Kay, J.C.R Licklider, Douglas Engelbart and the early hackers of interactive computing and the internet have not only invented the user interface as we know it currently, they have built and envisaged systems with true dynamism. Conceptually far beyond current tech. ²¹

Such systems are operating system and programming language at the same time, they are crafted in layers of cake, and they reflect the true malleability and composability of ideas themselves.²²j

[somehow something about how languages and interfaces are the same]

The comparative power of computational interfaces is an elusive topic and poorly understood. We know there exists a quality to well-designed systems, also called technical sweetness or elegance. It is currently understood mostly in the implicit technical feel, intuition or sense of aesthetics of programmers (and presumably engineers). What Paul Graham calls the The Taste Test [Succinctness Is Power], that is asking the simple question does this system of thought allow me to express what I want to express?.

I think most hackers know what it means for a language to feel restrictive. What's happening when you feel that? I think it's the same feeling you get when the street you want to take is blocked off, and you have to take a long detour to get where you wanted to go. There is something you want to say, and the language won't let you.

The hunch is that there is an objective quality that has to do with the succinctness or ease of expressing ideas, in the case of programming languages that are in conceptual spaces.

"The quantity of meaning compressed into a small space by algebraic signs, is another circumstance that facilitates the reasonings we are accustomed to carry on by their aid."

• Charles Babbage, quoted in Iverson's Turing Award Lecture

David Deutsch's philosophy of objective beauty comes to mind, which ties together with explanation structure making. [Why Are Flowers Beautiful?].

Whatever this quality is, it is fair to call it elegance (technical sweetness) for the moment.

• No objective meassure of elegance exists. And new kinds of philosohical perspectives must be taken before we can do so.
• Elegance has to do with the functional properties of the artifact. Elegant systems are robust, easy to understand, easy to maintain, easy to explain, easy to copy, they fulfill their function, they might be so self-evident that they are impossible to contain bugs,

• Elegance does not have much to do with perfection. Perfection is necessarily a counter factual, to which only approximations can be found (David Deutsch, Popper).

  In fact, elegant software might even contain a certain amount of sloppiness, depending on the 'what needed to get done'.

  It will contain shortcuts and so-called kludges, because time was expended more usefully elsewhere. And some some kind of sufficient level of function is reached.

• Elegance has something to do with 'getting stuff done'.

  The quote from Neal Stephenson's The Diamond Age, "signs that it was made with great care by one who was not merely motivated but inspired" might be misleading every so slightly. This great care is as much in the style and skill of the hacker as it is in the artifact. It is the fact that they can produce such a robust and well-designed system fast, that is the true elegance.

• Stratified systems tend to be elegant. That is finding a language of abstraction, which perfuses a space of problems, the problem domain with understanding.

  This is similar to the difference between Aristotelian physics and Newtonian physics. Where one is a patchwork of special cases and the other is a minimal, sufficient, abstract description.

It is pretty much obvious now that the brain is an information-processing device [McCulloch] that creates user illusions [Dennett].

—

Summary:

Vast amounts of tiny programmer memes that figure out how the computer they run on works, not because they set out to understand how it works, but because of the many that didn't mean the right thing, the ones that mean something useful is selected in a process of natural selection. In a substrate where your meaning is your connectivity. And the best memes are the ones creating magic interfaces for confident wizards. Creating user-level entities that use the computer with swag.

Audio diary version: Meme Machine Heroes.

—

If the brain has brain software running, we can go and understand the nature of this software, too. It is clear that it is a valid viewpoint to say 'the mind is a programmer, programming the computer it runs on'. Of course, whatever is programming cannot be competent by itself. It must bootstrap from simple elements. This by itself only kind of says the general idea of what is true though, it doesn't yet satisfyingly explain the mechanisms of what is this program, what the elements that program it etc.

I had this activity flow of the cell assemblies (see below) in mind and wondered how I could put this one level further up into the level of meanings. I came up with a thought experiment, called Banana maker matrix algorithm.

This is only 1 perspective of course (out of 12 required). But this really nits it together from bottom to top (both ways) for me:

Suppose we already have a nice meme engine, capable of trying out vast spaces of possible memes (and fast) and selecting them after some useful criteria.

Let's pretend for a moment this machine implementing a matrix algorithm, a big imaginary planet with a cyber-highway along the equator that makes you pass ten thousand cities, twenty times per second. ²³ So it's vast, but it's possible to traverse it fast.

Suppose further that there are simple agents in the system, let's pretend they are super-competent action heroes, who need to act on a clock. These people are very, very fast. And very competent and confident. Simply because we tried out many action heroes and the ones that were not competent and fast were discarded [Darwin, Dawkins]. Let's suppose for a moment that the memetic engine can select action-hero short movies. In effect you, as an action hero, have a short amount of time, to please the memetic engine in some way. Of course, you can hack its mechanisms, too, if you are competent enough.

You get created (some activity flow makes you spawn) in a place in meaning landscape that you already have supplanted with useful tools and so forth. Part of what a good meme does is plan for the next iteration of its existence.

The other thing you do is quickly go to some city, where many generally useful memes provide information for more derived memes. The most successful memes will be part of vast societies of contributing, more generally useful, memes. One might imagine some other meme leaves a sticky note on a wall in a city. They don't know why they do it and you don't know why they do it. But information can flow, and more useful information flows are selected. Say the meme-machine selects action-hero timelines, including the cities they move through.

Here is the next thing: This matrix has certain competencies, like spawning new cities and roads or manifesting a banana.

Everything that the computer can do, the matrix can do, and the memetic engine might also try it out.

Now and then the matrix will simply manifest a banana in a place, just to try out what happens. Kinda implementing an 'everything possible' mechanism.

Now you are a competent, optimistic action hero. And you trust in the magic of the matrix. 'I hold out my hand and there will be a banana'.

Again, the 2x2 matrix of optimism and competence, the optimism-drive, produces the following:

All the memes that are optimistic and pretend they know how to use the magic of the matrix, have a chance to survive. All the memes that are pessimistic have no chance and are discarded.

Optimism is not the only thing you need, you also need competence. In this case, it is the competence of the rest of the computer to provide you with what you needed at that moment to be a successful action-hero wizard (with swag).

     banana!   I don't know.
   +---------+--------+
   |   S     |   X    | competent
   |         |        | (the information flow is capable of creating a banana, if you ask)
   +---------+--------+
   |   X     |   X    | incompetent
   |         |        |
   +---------+--------+

S - memetic success
X - discarded

Commanding the magic of the computer without knowing how the computer works.

This holy overlap of competence and optimism is how there is a magic piece in this whole thing now. From the thousands of memes that said 'There is a banana', there is one meme action hero that confidently puts out her hand and says 'there is a banana', and the system tried out randomly what would happen if there is a banana in that place - this short story movie timeline then was especially useful to the system and selected. Because for some reason a banana is what this hero needed to be successful in its matrix short-term movie.

Nobody knows why: The memetic engine sees an information flow that worked well, and the hero sees the matrix that made a banana for her, similarly, the banana memes are just information flow pieces that had a chance to be active, and they do what memes do. Trying to be active more. The interface between the user and the lower software world is something like 'competent query' and 'competent result', where both are selected memetically afterwards, so that the matrix becomes more and more magical to use (obeys every whim so to say, and is very useful, retrieves from midterm memory for instance), and the user-level entities become more and more competent, artistically elegant, on-point, demanding, confabulating, and confident.

It is that the memetic-engine favors competent information flows. And importantly that means that high-level wizards, artists, and action heroes move confidently, using the computer in ways they don't understand. For if they would need to understand, some other meme would simply be faster than them. This creates competence hierarchies, where the higher agents in the system delegate the lower agents. To put it the other way around; The more generally useful memes have a great memetic strategy - be so useful that all the higher-level agents can't help but incorporate you into their short-movies. So that you are 'on', memetically selected, in many situations.

The memes that will be most competent are the ones that use the magic interfaces of the system, not knowing how the computer works. They don't have time to know how the computer works, they are busy with creating cognition.

When a high-level meme delegates lower-level memes to provide it with information, we can imagine little hacker wizard memes, in the substrate of the matrix so to say, that either figure out how the computer works and provide you with competence or not figure out how the computer works. Then they are discarded by memetic selection. Of course, they don't know how the computer works, they either represent a wiring that has meaning, or not.

The memes don't know what they mean when they start, it is only afterward that the meaning that made sense is leftover. This is the strange inversion of reasoning of Darwin's idea. Taken seriously for software-level mechanisms of computers that run meme-engines.

On the highest levels of this hierarchy, you have memes that are so competent, that they command everything that the whole computer can do with precision and artistic swag.

This then looks like a mechanism for a memetic machine to perfuse its computer with competence, to build information processing hierarchies that will use the computer, completely and competently.

Of course, the matrix machine is only a thought experiment, the matrix algorithm that runs on human brains is the one we know ourselves. What I like to call the 'cognition machine'.

I submit that this magic interface is the same class of software as our magic interface. Brain software is a magic banana maker matrix meme-engine.

And the brain has something to do with navigating the world as an animal. Not because in principle all meme machines are about that, but because this specific meme-machine evolved biologically. (So the wires that shape the memetic landscape are biased to be about navigating the world and being an animal).

A meme machine can create user-level abstract entities that have magic interfaces to the rest of the computer. Fulfilling the basic requirement we had for a brain-software.

—

When you go and wonder what is brain software, you probably will come up with something that sounds a bit odd at first, otherwise, you would probably be doing something wrong.

—

Those memes are allowed to get arbitrarily tiny, down to single-unit (whatever that might be) stupid computations. It's kind of sweet (technically sweet) to have a mechanism where arbitrarily abstract entities are allowed to coexist and refer to each other.

—

Shoulders: Darwin's strange inversion of reasoning, McChulloch to say that the brain is an information-processing device, Dawkins abstract evolution to replicators

The other important ingredient is the power of interfaces. This is the power of what software is in the first place, especially pleasing to a software developer.

An important aspect to keep in mind is that the brain seems to be doing these high-dimensional, highly parallel computations. This is not a synonym for 'very powerful computer' but this has a very distinct computer science and cybernetics to it. This is the topic of the computational models of things like cell assemblies, that I explore here and elsewhere. (Current work). The main point is that 'everything possible a little bit' is a valid thing to say in such a computational system.

Don't limit your thinking to the computers you experience, only expand it, never make it smaller.

Similarly, with 'software' I don't mean the buggy, half-working user interfaces of current primitive tech. There is a larger view, that we are only glimpsing. It is deeply philosophical and would answer questions about the nature of abstraction, life the universe etc.

A cybernetic level of understanding of what software is, is just in its infancy.²⁴

—

With this space of thinking you can muse about civilizations of memes, that build vast stores of knowledge. And archeologist memes going through the libraries of eons of accumulated knowledge.

Harmonious memes, cooperating memes, that never truly meet across times and space and yet, together contribute to the budding ideas, that only artistic memes and old wise-wizard memes can glean from the flow of time and history.

Social attractors of a kind that are bigger than a single meme on its own.

I am in the camp of people assuming that the interesting part of cognition happens in the cortex. In other words, it looks like modeling the function of the cortex is a path toward a model of cognition.

Givens:

1. Injuring the cortex makes a person lose specific capabilities.
2. The cortex is the thing that explosively blew up in human evolution. 1a. Whatever is special about us is almost certainly special because of the cortex.
3. The thalamus has 10⁸ neurons; this is an upper limit for cortical inputs.
4. Cortex neurons have 10¹⁰ inputs. -> This means that 10x maybe even 100x more connections to the cortex are from the cortex. (Braitenberg 1986) The stuff that cognition is is mostly stuff about the mind itself (reflexive). No wonder hallucinations are a thing.
5. The cortex is more generic than other parts of the brain, it looks as if evolution found some basic building block (cortical columns?) which when duplicated, made more useful intelligence in the animal. -> Both the given that the cortex is generic/uniform and the given that areas are different is interesting.
6. To explain cognition means to also explain altered states, out-of-body experiences, dreaming, mental pathologies, etc. (Metzinger).
7. Everything with a brain also sleeps afaik. Roughly half of a good model of what the brain does concerns itself with stuff that happens during sleep. (we will see that this move makes us able to happily move this or that part of a cognition mechanism into a dream phase).
8. The mind is self-assembled, without an external loss function.
9. The mind is online and has resource constraints. Unlike a computer, there is generally not a pause when you ask a person something. The mind cannot stop reality in order to process some input. (But the existence of attentional blink shows us that a tradeoff in this space is being made).
10. Whatever the basic building block of the cortex, it grows in the first 2 dimensions but not the height. Otherwise, we would have evolved more cortex volume, not surface area (Braitenberg …)

—

1. Neurons can represent information states, neurons are universal [McCulloch and Pitts 1943, Church-Turing]
2. Layering information representations leads to higher abstracted information representations [Rosenblatt]
3. The same idea is the 'feature detectors' of computational neuroscience [Hubel and Wiesel 1960s]
4. We sort of know that cognition machines are meme machines [Dennett 2017]

Reasonings / Intuitions:

The cortex is an information mixing machine - Braitenberg

• When thinking about the cortex: a. We are allowed to use the rest of the system as an explanation. The cortex is about itself. -> The explanation is allowed to go in a loop.

  b. We are allowed to use an abstraction barrier and explain a part (a juggling ball) in terms of stuff

• The cortex is like an ocean. This is part of the point.

+------------------------------------------+
|                                          |
|                      |    ^              |
|               ====== |    |      <-------+-----+
|               Cortex |    |              |     |
|               ====== |    |              |     | about itself
|                      |    |              |     |
|                      v    |              |     |
|                                 ---------+-----+
+--------------+                           |
|              |                           |
+--------------+--------------------------++--+
+--------------+                          |   | eyes
   rest of the brain                      +---+

Why is there a big fat cake of stuff that is about itself on top of the brain? And it seems to have to have to do with what we call cognition and intelligence.

What is [B], the brain, about? Easy to see for vehicles 1,2. There are simply sensors 1:1 about the world. With vehicle 5 we enter the world of being able to stick in more information processing units in between sensor and actuator. The interneurons of neuroscience. With that move, we have an internal world, and some pieces of our machine can be about that, instead of the sensor input.

The cortex is mostly about itself. If you look at a piece of cortex, you will mostly see neuronal units that are about… some other piece of cortex. Their world of a cortical neuronal unit is mostly the rest of the mind!²⁶

I used to think hey this or that. Maybe Glia cells are important, maybe the neurons do computation inside themselves, who knows?

I have refined my model to simply take pyramidal cell activity as the center and move on.

1. If something else than neuronal activity is how the brain works, then why is activity what drives muscles? It seems much more biologically harmonious that whatever is making muscles move is also what the brain is about. (Turns out that is pyramidal cell activity).
2. If neurons do computation inside (microtubules? quantum computing…?, pixie dust at the synapses?) then how do you get the information from outside the neuron into the neuron and back out again? I see you need to solve the same engineering problem that the brain is solving in the first place, a second time. (I.e. how does the information go into the neuron and out?).
3. Some sensor -> motor reactions happen in a time order that leaves time for only 1 or 2 action potentials through the brain. (Braitenberg 1977) Everything looks like there is nothing faster than action potentials that can move through the brain. The pyramidal axon, in general, has an evolutionary drive towards transducing action potentials as fast as possible. How would this fit with a view where something else than neuronal activity is doing the computation? Just the aesthetics of these 2 ideas don't fit in my opinion.
4. Tissue with brain lesions is filled with glia, and this tissue does not substitute the brain function as far as we know. (Braitenberg 1977) This does not bode well for a model where glia calls are doing anything essential for cognition.
5. The hippocampus has the most cells in the cortex [iirc], it is often the initiator of epilepsy. (Braitenberg 1986) Whatever the hippocampus is doing with all those cells then, it has to do with excitatory neuronal activity.
6. With spiking neuronal activity you can put short-term memory in the units. Just make a circuit that goes in a circle. The simplest version of this is a pair of neurons going back and forth. This is a memory implementation. -> This all looks like neuronal activity can make short-term memory
7. If I want to make a lot of short-term memory and have an evolutionary driver towards mid-term memory, I get something like a Hippocampus. Consider a cluster of cells with an evolutionary driver to make activity back and forth to store some previous state. a) Hippocampus has a lot of excitatory cells, so you can store a lot of 'activation'. b) If you want to abuse the circuit to simply make activity back and forth, it is useful to slow down the spiking rate. Hence, it is not surprising to me that the hippocampus has slow theta wave activity. c) You have another problem to solve, this ongoing memory activity should not spill to the rest of your (activity-based) system. So you curl up that piece of neurons and make it anatomically separate from the rest of your cortex. Related to this, you get the problem of epilepsy, you need to manage this immense activity of neurons now.
8. We know some visual cortex circuits and they work with pyramidal cell activity.

Everything looks like and nothing does not look like the main substance of what is doing cognition is the activity of the (pyramidal) cells.

—

It is not the engine, the gasoline, the wheels or the steering wheel that is most important in a car.

If you say 'the synapses are the most important part'. I only need to make this analogy and I am at peace. The only thing that truly matters is that you don't bring in something non-essential because that is just wrong.

If the activity is like the gasoline, then what is the rest of the engine?

The story of Hebbian Plasticity is a remarkable triumph of reason and imagination.

Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. … When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.²⁷

Hebb [Hebb 1949] came with psychological reasoning and imagination about what neuronal tissue could be doing to explain what we observe from studying animals.

With something in mind to look for Eric Kandel went and found the biochemical basis for Hebbian plasticity, the work that made him win a Nobel prize.

—

It is important to note that fire together, wire together leads to a slightly wrong idea.

It is that every time I fire, I can look at who activated me - and those synapses I can strengthen. So there is fundamentally a causality in this notion, not mere juxtaposition or mere association. The rule needs to look at the activations of 2 time steps, not one.

It should be called Your fire is my wire, I wire if you fire, fire me and wire me, They who fire, make me wire, The more you fire, the more I wire, When you fire, I will wire, I am looking for some fire with my wire, I want to plug my wire in your fire.

[insert history, Turing, Lorente, Hopfield] [cool history part by Rafael Yuste here: Rafael Yuste: Can You See a Thought? Neuronal Ensembles as Emergent Units of Cortical Function]

The brain is playing with fire.

Tery Sejnowski²⁸

The meaning of this is that the brain works with positive feedback, and excitation, not negative feedback like the control systems we build. The fundamental problem coming out of this organization is epilepsy; Which unsurprisingly comes in many different forms.

The fire is igniting the network, and the biology of the system must keep this fire in check.

The second part of Hebbian Theory is the Cell Assemblies. The cell assemblies are well-connected sub-networks that support their activation together.²⁹

In order to make something interesting, we need to limit the amount of activation, which Braitenberg called a Thought Pump ³⁰. Another plain name might be inhibition model. (But the term thought pump is so daringly far-reaching, I love it).

This fire; survives, or it doesn't.

Epilepsy is a memetic problem: The simplest meme says 'activate everybody'. This suddenly gives us the perspective from one abstraction level further up: The machine that shapes the memes.

Imagine those cell assemblies, supporting each other, evolving strategies of survival etc. Survival means I keep on firing together with the people that activate me. They are replicators, they represent knowledge about how to replicate across neuron timesteps. They are memes [Dawkins 1976] made from neuronal substrates.

Note: These are somewhat different memes than the cultural unit of imitation. These memes here, the cell assemblies are the replicators of computational neuroscience. They have to do with cultural memes, but only because they are part of the functioning of the cognitive computer the cultural meme is infesting.

An inhibition model is a memetic landscape shaper. It says 'I make it harder for memes to exist'.

If you shit on all ideas evenly, only the good ideas will survive. This is the basic notion of a thought pump. And a doorway into neuro-physiological conceptualizations of memes and their substrates.

This activity flow needs a strategy now, in order to win out against other activity flows - else it will not win in the competitive environment that the thought pump creates.

Of course, it does never have strategies the way we do, its strategies are free-floating rationals and it has competence without comprehension [Dennett 1995].

The activity flow of neurons that support their activity together, is subject to the laws of natural selection. They are replicators, abstract entities with an agenda.

The computational substrate I have in mind is designed to support memes:

1. Make activity scarce
2. Make the activity mean things by connecting it to sensor and motor destinations and sources.
3. At each time step, choose the best-connected subnetworks. I.e. make the subnetworks compete for connectedness.
4. With 'online' plasticity, you get immediate associations between cell assemblies. Like playdough that sticks together.
5. Mutal self-excitatory pockets of activation emerge from this arrangement. The Cell assemblies can be seen as a data structure in a high-dimensional computing framework².
6. We can see the cell assemblies as replicators[Dawkins], replicating across neuron timesteps. How well do they replicate? It depends on their connectivity, i.e. their meaning.
7. Use memetic landscape tricks, that bias the system towards useful memes.³¹
8. Optionally: Prune away everybody who is not active. [Literature on 'perceptual narrowing', and Neural Darwinism]

[This is all biologically reasonable]

The working hypothesis:

The structure and function of Cortex is to implement an assembly calculus.²

—

Memetic landscapes?

Ideas so far:

• Control all sources of activity well, they make the meaning of the meme space.
• If you make the prime source of activity the sensors, you get memes about the world.
• If you reset the memes in a short amount of time, you get memes that are competent in a short amount of time which is a memetic driver for abstraction and hierarchies.
• Now you have memetic drivers that make memes spread into areas/ nuclei that are not reset. If you make it hard after some criterion to spread into such nuclei, you select what kinds of memes you get.
• If you make a slow meme-place, everybody wants to spread into that place, that is an evolutionary driver for midterm memory. Simply make a place where memes survive for long periods, they will compete to get there.
• You can implement reward by activating a sub-network that makes up a meme, it is tempting to assume that some gamma activation would come from such a rationale.
• You probably want to hook up to some Darwinian wires, that tell you 'this has to do with survival'. This way you can reward all the memes that the Darwinian brain approves of, i.e. you change the memetic landscape to favor memes that are useful for survival.³²

—

Consider the simplest network of 2 neurons A and B.

[ A ] [ activate? ] [ B ]

The meaning of the network is the connections. It can mean A activates B, B activates A, or both, or none. Disallowing self-connections for the moment.

Consider the basic optimistic meme: 'I will stay alive', or 'I will be active'. We can draw the success matrix from above again.

activated! not activated
+---------+--------+
|   S     |   X    | competent
|         |        | (the meaning of the network means I stay active)
+---------+--------+
|   X     |   X    | incompetent
|         |        |
+---------+--------+

You need to be optimistic and competent in order to have success, or else you are discarded. The only meme that will survive is the one that says 'I will stay active'.

From memetic theory then, we can see that this network will stabilize into expressing the notion that 'A and B activate each other'. I.e. those are the connections that will survive and strengthen, everything else is discarded.

—

Kinda work in progress reasoning.

Let's assume for a moment that we can support memes with temporal structures. That is a cell assembly that waxes and wanes and represents a succession of states and so forth.

Here is a wonderful strange inversion of reasoning, it is that the meaning of memes happens before there is the meaning of memes.

Let me explain:

Suppose for the moment that virtually all activity must flow from the sensors. We see the first layer of memetics, which says 'Sensor active'. Since the simplest meme says 'I will stay active'. The simplest meaning is 'This sensor will stay active', in other words, 'The world will stay the same'.

Let's imagine a memetic unit (whatever that is, maybe a cell assembly) saying "I survive", in some derived meaning space. I.e. it is a space of possible meaning space, that is about the sensor memes, about the spatial and temporal structure of sensor meanings. The memes don't know yet what they mean, it is that the only memes that will survive are the ones that have competent connections I.e. they connected to the sensor memes in such a clever way, that they represent something about the world that stays the same, even though the sensors change!

To see why this is true consider that each meme competes for sensor activity with alternative memes. We can also imagine competing with a null hypothesis element. That says 'If you are not connected well enough to some people that activate you, the null hypothesis will be on'. These are thought pump considerations, making a more competitive environment for memes.

We can further imagine another layer of memes still, that simply stays on, because they connect to other memes with a temporal structure. Thereby representing the essence of some ongoing situation.

1. These memes have a reason to find essences in situations.

2. This search is memetic:

   First, you have many possible memes, all saying that they are eternal truths.

   Second, the wrong ones are discarded. And the ones that mean something useful about the world stay.

It is tempting to muse further. What about selfish memes that simply activate from all over the network? Maybe the answer is that they exist, and their meaning then is 'The mind exists', or 'The self exists', or 'The world exists'.

Maybe it is those memes that can give us the sense of something vaster and greater and eternal existing in the universe, our minds, our social realm, etc.

Maybe it is unallocated meaning units, that look for a reason to exist; Maybe it is some of those meaning units that a person can attribute things like god, or the fundamental spirit of nature or something.

—

Why is the basic meme 'I am eternal truth'? It is because of the optimism-drive reason from above. A pessimistic meme will be less successful than an optimistic one. It is the holy overlap between optimism and competence, that is favored by natural selection.

To relax this one further, it is useful to consider the basic meme 'I will be thought again'. This opens the door for what I call strategic memes (below, future). Strategic memes are memes that bias the system to be expressed again, not by staying active but by playing longer, cleverer games. (It was of course obvious from memetic reasoning that they should exist. We see that cell assemblies like this would exist, given the properties of the network above).

—

The basic possible rules of meme machines will create an environment that biases everything towards useful memes, that is the problem that the basic wiring of the brain somehow needs to solve.

One of the most important problems a meme machine has to solve is selfish memes that don't mean anything useful. So we can expect much of the brain's functioning to be solutions to this problem.

I would submit that describing, categorizing, fabricating, and transforming memetic landscapes is one of the basic problems of cybernetic psychology or 'synthetic memetics'.

—

Similar to this you might say all memes are a mix of static and dynamic activity flow. This idea comes up in Neural Assemblies by Guenther Palm.

—

A cell assembly implementation (online) A random directed graph with geometry(neighbors have a higher chance to be connected), without plasticity. At each time step, the top k neurons are allowed to be active. You can move the mouse to produce different inputs. (inputs in red). Only one cell assembly will 'win out' generally in such a setup.

With such an inhibtion model, each cell assembly implicitly inhibits its alternatives, since only one is expressed.

A striking aspect about such ambiguous scenes, like this or the necker cube, is that you flip between interpretations, but you never see both interpretations at the same time. The system somehow 'decides' on one interpretation.

—

The basic meme strategy is the one that makes cell assemblies in the first place: Activate your activators.

Since meaning-level memes and 'sensor level' memes stand in a relationship, and since we introduce the inhibition model, which makes the system decide between some cell assemblies, you get these prediction/interpretation shapes:

        -----------------
         \             /
          \     A     /                   meaning-level
           \         /
            \       / <----->   B
             \     /   inibit
              \   /
             --\-/---------------------
                X                        sensor-level


A - Stable cell assembly

We can observe:

• You can produce perception states by stretching cell assemblies across the meaning level and sensor level.
• The meaning level parts of cell assemblies will bias the system towards 'perceiving' the world a certain way. (Activate your activators).
• If meaning-level cell assemblies compete via something like the inhibition scheme of the model above, it is simply the nature of the mechanism that one wins out, never multiple.
• I submit that this fits well with the emerging view of cognition called 'predictive processing', [Andy Clark, Anil Seth, etc.]
• The inverted pizza piece, or the inverted ice-berg:
  • Perception is a meaning-level construct.
  • The meaning level is much larger than the sensor level (i.e. most connections in the cortex come from the cortex).
  • The meaning level can take larger situations into account. Perhaps the basic reason for having a cortex in the first place.

Perceptions are stable memes, everybody saying 'I am true'. But only the ones that get enough activation flow support from the rest of the system, including the sensors, win out. I.e. the meaning of a meme is its connections. The connections are allowed to be random at first since we can select the meanings afterward via natural selection. In a system where most activation flows from the sensors, this will select the meanings that represent true things about the world.

Paper Cell Assemblies in the Cerebral Cortex, V. Braitenberg 1977 ³³.

I am using the term neuronal unit interchangeably with neuron. And the term neuronal area interchangeably cortical area.

Cell assemblies are subnetworks of well-connected neurons that activate each other. In an environment where activity is kept in check (via the inhibition module), only the cells that activate each other well will stay active. The inhibition element might work by setting a threshold of synaptic input, below which neurons will not be active. (Threshold control). Note that a simple inhibition model would be 'cap-k', saying that the top k neurons are allowed to be active.

The fundamental property you get from this is what you can call 'pattern complete'. We say a cell assembly can ignite from activating a subset of its neurons.

A cell assembly lives via the excitation of its elements.

Via Hebbian plasticity, 2 cell assemblies happening at the same time, will associate, we will find a subnetwork that is connected to both.³⁴

From one perspective, we can say that there is only ever 1 cell assembly in the whole brain. Intuitively, you can imagine lowering the threshold maximally and getting all neurons to fire. This doesn't have to be this way, it depends on the connectivity of the network.

This would be the 'maximal cell assembly'. Presumably, that is the whole brain (that would be epilepsy).

The notion of a cell assembly only makes sense if you take the inhibition into account. Otherwise, it is hard to get to the notion of a piece of activity that survives on its own, because the elements activate each other.

A cell assembly might be composed of sub-cell-assemblies, that is, there are well-connected subnetworks of active neurons, which might form a well-supported cell assembly on their own. We say that the cell assemblies have multiple centers.

Braitenberg mentions homogenous and non-homogenous cell assemblies.

A homogenous cell assembly says that each neuron is connected with the same weight as everybody else, this is a theory-only construct, I think, to point to the non-homogenous cell assemblies: In a non-homogenous cell assembly, there are subnetworks of neurons that are better connected to themselves than to the rest. Again we say that such a cell assembly has multiple centers. Where a center is a subnetwork that would be able to support a cell assembly on its own.

Excitability is simply the inverse of the threshold; (I also called it eagerness before encountering the word).

If my threshold is low, I am eager to ignite.

If my excitability is high, I am eager to fire.

This means that you only need a little bit of input (synaptic inputs) to 'ignite'/fire.

This is simply to make it easier to think because sometimes I want to have that perspective.

If you imagine a cell assembly of mutually supporting neurons (a cell assembly center) and now you increase the excitability of the neuronal area, you will see that additional neurons are firing, the more you increase the excitability. These are not necessarily part of the center. They don't need to support the cell assembly at all, they simply ride on the existing activity, without feeding back to where it comes from. (Note that you only get this kind of activity with a low threshold. If the threshold is extremely high in the system, only the core of the best-connected subnetworks will be active - i.e. they all activate each other strongly).

We call this the halo of a cell assembly. The halo is like the outskirts of the cell assembly city. Where the center is the city center. It supports the activity of its halo, but if you increase the threshold, you will narrow the assembly down to its center.

For some reason, this makes me think of Arthur C. Clarke's 2nd law:

The only way of discovering the limits of the possible is to venture a little way past them into the impossible.

From this, you can craft a hypothetical mechanism that finds well-connected ideas, ideas that fit with the rest of the situation of the brain.

Say you have some inputs coming into your thinking goo (neuronal areas), that activate a subset of your neurons I. You will form a cell assembly from this. Call it FI. Note that the neurons that are part of the cell assembly don't have to be the ones that listen to the sensors in the first place. (With a random graph they are not, see my programmatic versions of this).

What you can do now is increase the excitability, activating a cell assembly E(FI), this is the union of FI and FI-halo. This represents everything, more or less, that is sort of associated with the inputs your sensors pick up. Maybe that stands for the 'excited' FI.

E(FI) will almost certainly be a non-homogenous cell assembly, containing multiple, maybe many, many [depends on the network] centers. One of them is the initial FI, others are BI, CI, … From cybernetic intuition, it is very likely that FI is not the strongest of these centers. I.e. there is some other sub-cell-assembly in E(FI), that has a stronger mutual activation. I am not sure yet and there many perspectives to take to make this super plain and obvious, but the intuition here is that there is a 'better idea' available; That there is a solution to the overall situation so to say, a meme (cell assembly) that has the right connections to be supported in the current situation (since the meaning of the world is in the connections of the network - it's quite wild, yes).

One thing to consider is that the rest of the cognition machine is making all those top-down cell assemblies.

Since those participants in the system are activating their activators, the shape of the lower level - E(FI) is determined by the larger situation at hand.

I.e. whatever is well supported by the rest of the system is activated. In other words, you bias what is possible by taking the larger situation into account. In other words, the system has a vague idea about what is true, you will not be a good meme, your connectivity will not 'make sense', your cell assemblies will not be supported, if your meaning is something completely else than what the system thinks is roughly true.

If I am a cell assembly that says 'Here is a face', I go and activate the lower perception people to represent inputs that look like a face. So E(FI) has a shape where all the activity that fits with the notion 'here is a face' are more active. Note that 'good idea' here means very specifically a subnetwork of neurons that happen to be connected in such a way that they get activated by whoever is active right now - stronger than alternative sub-networks.

We can observe that this is true by imagining 2 competing centers in E(FI), say BI and CI. BI might be slightly better supported by some top-down connections from the network. In other words, BI has friends higher up that support it.

The second step, as you might have guessed, is that we increase the threshold again. This will select the best connected sub-assembly from all possible centers inside E(FI). Finding for instance BI. [I can show programmatically how you get the best in a simple model. This is quite intriguing. And fits with the notion that something immensely parallel is happening in the brain].

This mechanism is what Braitenberg called a thought pump. (Note that this is a slightly different one from the one above; In "The Vehicles" the thought pump just meant a threshold control. In the 1977 paper, 'thought pump' means a very specific dynamic threshold control that searches for well-supported cell assemblies).

Now you can imagine a process where you increase and decrease the threshold in quick succession - perhaps creating the alpha, beta, gamma EEG waves. I.e. perhaps beta or gamma frequency represents the 'upper' step of a 2-step oscillation.

You will get a sequence of cell assemblies, I imagine a ball moving around in meaning-space. Perhaps it is especially interesting to have the ball move around then then suddenly stay. If it stays, you have found connectivity in the network that is the 'best-supported idea' of the network.

Or you move around the ball and it suddenly grows. This is a 'good idea that fits many things'. G. Palm is musing that this is maybe a mechanism for good jokes, too. Note that a good idea is an idea well supported by the current situation.

Another parameter would be the number of time steps you wait until you declare something 'the best interpretation of the situation'.

And another one would be the amount of activity that you declare a 'good idea that fits well'.

It looks like it is possible to shape a network memetically to represent the world (see sensor flow musings above, and predictor mechanisms, coming soon). Perhaps you build a network that represents the meanings of the world, and then you use that network to find further meanings again.

The thought pump can ask the existing network 'What fits?'. If the network grew from the sensors, then it has to do with real things. 'What fits' will be something that 'makes sense', that is realistic from many angles.

—

It is interesting to consider that there are 2 sources of time and causality with those cell assemblies.

1 is that I connected A->B via Hebbian plasticity, which means that if A is active, it will flow activity to B, in the next neuron time step. This is the fastest time representation available with that substrate, then.³⁵

2 are the thought sequences of the thought pump. It seems like this needs at least 2 neuron time steps. 1 to increase and 1 to narrow down the activity flow. This doesn't mean that 2 is always faster than 1 because you have different neuron frequencies, varying by more than 2x,4x,6x or something.

—

The idea that you can lower the eagerness and get more fine-grained thought is quite intriguing. Consider that only the cell assemblies that fit the situation especially well will survive.

What you can do now is find an especially well-connected assembly. That one is still large, even though the threshold is low.³⁶

—

Perhaps Hofstadter's abstraction ceiling (see his Strange Loop talk) is a brain feeling created from the situation of increasing the threshold very tight, without the wonderful release of a good idea.

Perhaps dopamine modifies what counts as a good idea. The bar for connectedness is lower so to say, biasing the system towards action, not deliberation. Perhaps psychosis is that you take the first good-looking idea and believe it, even though it was not the best idea you could have come up with at all.

—

I am a Murray Sherman fan; There is some cool work they did to establish these things empirically [Talk 1, 2, 3 these are all super dense and elucidating].

Also, I'm only picking the 50% that land on my views here; And probably leave out the most important parts.

The cell assemblies then, provide a perspective on the structure and function of the cortex and its nuclei. (1 out of 12 required).

The cortical network is the meaning, but the ideas live inside the network.

Pieces of mutual excitatory sub-networks compete for activity sources. These can also be seen as stable data structures in a high-dimensional computing framework [Dabagia, Papadimitriou, Vempala 2022]², representing the inputs.

The biology of the cell assemblies is a biology of hyper-dimensional, agenda-having, knowledge-representing entities, operating in subsecond timescales.

The basic tenants of this cell assembly memetics (what I have come up with so far):

• A random directed graph with an idealized inhibition model, discrete time steps and Hebbian Plasticity [Hebb 1949, Kandel 1965] implements assembly calculus [Vempala 2022].
• After the presentation of inputs to the network, a stable representation (called cell assembly) of the inputs forms rapidly.
• (Note that we are allowed to require on-the-fly plasticity [from neurophysiology])
• Cell assemblies are hyperdimensional: They are made of many small data points. Pattern completion is the basic operation of Cell Assemblies.
• The timescale is sub-second. We only need a few neuron time steps to form stable cell assemblies. [per theorem, Vempala 2022]. (For igniting existing cell assemblies from partial inputs, I suspect you get a substantial part of the cell assemblies after a single timestep and the full one after very few. That is just from me hacking around with an assembly calculus model.)
• Cell assemblies compete with their alternatives, with the best connected sub-assembly winning.
• Assuming an idealized inhibition model, we can select the best connected cell assembly at each time step. [Braitenberg 1977]
• Cell assemblies are subject to natural selection, cell assemblies are replicators with tiny agendas. I.e. assuming many possible connections initially, well-connected sub-networks compete for well-connectednes. In the context of the driving input activity. In other words, stable cell assemblies are the ones that exist. We can see that cell assemblies are replicators, replicating themselves across neuron timesteps. (But strategic cell assemblies exist, too. Biasing the rest of the system to be active again, somewhere in the future, not necessarily the next neuron time step). Note that gene-centered and extended phenotype views [Dawkins 1976, 1982] apply to memes, too. Giving us a large explanation lever on their structure and function.
• Activate your activators is the basic memetic move. (The basic property of a cell assembly).
• Memes are selfless: Memes will merge, given the chance. [from assembly calculus]. With assembly calculus, we can activate 2 cell assemblies together, and find an overlapping cell assembly. Another way to put this is that symbiosis is a primitive operation for the memes. Mutual activation is literally how the cell assemblies are formed in the first place.
• Causality is the most fundamental concept, (see Hebbian rules, it is a 2 time step concept)
• Cell Assemblies have temporal structure, or "causality flow" unless their activity goes in a tight mutually activating subpopulation.
• I.e. association without direction is when the activity goes back and forth.

Conjectures:

• Inhibit your alternatives is the second basic memetic move. (and supported via thalamic inhibitory interneurons, innervated from cortex layer 6, see Contrast And Alternative)
• The meaning of a meme is the connectivity of its sub-networks. (This is only useful if the activity comes from the sensors and ultimately moves effectors).
• The basic agenda of a meme is I will be thought again, or I shall stay active.
• Memes don't know boundaries (memes spread into all wires available).
• In principle, the cell assemblies don't care about some other part of the brain. They only care if they have to compete with somebody else that is exploiting that other part.
• All wires are allowed to be 'kinds of wires'. The actual connectivity is allowed to be random, because in the second step, the memes will populate the network, and non-sensical wires will simply not be active.
• All circuits in neuroscience can be understood in terms of the game of the circuit, the memetic games and the drivers a circuit creates for cell assemblies to play.
• Because of the circuitry (see Contrast And Alternative on this page), whatever meme wins out fastest will go and inhibit its alternatives. Whatever meme is fast wins. (But perhaps during dreaming only parts of cognition are tried out, reducing the grip such a 'best meme' has on the system).
• The computational paradigm of cortex is simply high-dimensional interpretation or representation, pattern complete, "template filling-the-blanks"; Or 'ongoing situation analysis using best guesses of explanation'.
• Perhaps the memes can be said to participate in the interpretation game. Whatever memes are representing an expectation structure that 'fits' the current situation, are on.

The inference that can be drawn from this is that the relevant "things" of our experience are represented within the brain not by single neurons but by sets of neurons. Just how many neurons are involved in the internal image of a thing like "my house" or "the neighbor's dog" or "the tune of Greensleeves" is very difficult to say, but it is likely that their number is too large for any neurophysiologist ever to be able to record their activity by multiple-electrode recording. The sets of neurons that stand for certain events, or objects of the outside world, the "cell assemblies" as they were called by Hebb⁴⁴ must have a certain internal coherence, since it is one of the basic observations of psychology that partial evidence of a thing tends to make us perceive the whole thing. This is best explained by supposing that the elements of a cell assembly are coupled by excitatory synapses, so that the excitation of some of them ignites the whole set and leaves all of them in a state of excitation until their activity is again extinguished by external inhibition. If we want to localize cell assemblies more concretely in the cortex, we notice that they are of two sorts - restricted to particular areas, or diffuse. The cell assembly "my neighbor's dog" represents within my brain a very complicated bundle of properties in various sensory modalities and must occupy almost the entire cortex. On the other hand, the cell assembly "Greensleeves" may be localized in the auditory region of the brain and may indeed persist undisturbed by any other activity my cortex may be involved in.

Some more properties of cell assemblies come to mind. Some are essentially synchronous, like motionless visual images, others have a temporal structure, like the cell assembly representing Greensleeves. The synchronous ones are composed of parts any one of which may recall the others, while the asynchronous ones lack this symmetry, since obviously it is much easier to recall the tune of Greensleeves starting from the beginning than from the end. However, some temporal structures, such as the words of a language, seem to be represented almost in a synchronous way, since it is about as easy to find words that rhyme with a given word as it is to find words beginning with the same letters: the asynchronous cell assemblies representing the words can be activated in the direction of time as well as in the opposite direction.

(Braitenberg 1977)

Note that a model that talks about using the whole cortex for single concepts is very much out of favor in current cognitive neuroscience. But until they have a model of cognition that talks about what areas are doing and why concepts don't span Cortex, I just assume that the anatomy and the functioning of the Cortex come across like it is mixing notions of local with global information processing flows. Perhaps the ultimate notion of global processing on this page is one of the holographic encodings of the cortex.

Either way, when thinking with cell assemblies, we are freely assuming that cell assemblies simply span the cortex. We can say there is a global cell assembly, made from all the current sub-cell assemblies. This is simply a matter of terminology.

With the layer 5 movement output from A Curious Arrangement in mind, how does the brain achieve:

• Having a model of the relative places of objects in the scene.
• Have a stable representation of the scene. It stays when I move my eyes. (Even though the pixels on the retina all change).

I move my eyes and I see an oval shape of color and shapes that I label the visual field. I can move to the edges and there the shape of this field changes somewhat, scued to one side. But in general, this field just stays - it strikingly stays the same.

Presumably, Cortex can offset the change in retina sensor input, taking into account the eye movement data, presumably coming from the efference copy from A Curious Arrangement. The layer 5 circuit tells us that this efference is then represented in higher-order thalamic nuclei, (pulvinar..).

It seems parsimonious to assume that the higher-order visual areas represent the visual scene and that all those representations stay the same when moving one's eyes. When I look ahead I see an object. I can move my eyes and I feel the object stays in the same place. There should be a stable cell assembly that represents the location of this object somehow if we can be so naive. It is stable across eye movements.

The stuff that changes is then pushed down, to the primary visual areas and retina, everything else is allowed to (and usefully so) to be stable.

I was thinking of some wizards sitting together in a city, they are looking at the clouds (retina data) and are trying to predict how the clouds move. They have a magic sun deal (eye movement information), which says north-east, east, and so forth (with an encoding of degrees and distance or so forth).

Something meme-centered struck me; What if every object meme simply outputs the behavior 'move your eyes to me'? (At layer 5, presumably at the cortex that is usually labeled as the object where cortex).

                    visual field
        +------------------------------------+
        |                                    |
        |   +---+                  +---+     |
        |   | O1| <-------X------> | O2|     |
        |   +---+   x->o1   x->o2  +---+     |
        +------------------------------------+


X -  current gaze
x->o1 - eye movement encoding, how to move gaze to O1
x->o2 - same for O2

Then, all where information is allowed to be encoded in the eye movement that would make this object be in the center of the gaze. From memetic reasoning, an object-representing meme should benefit greatly from being looked at. (I am mixing the internal world and the external world freely, see Macrocosm and microcosm in the first part of this page why we can make this move when talking about cell assemblies). I.e. it would make sense if every object meme would play a game of the gaze trajectories.

I think there is something about looking at things like the stars in the sky. But this can be any arrangement of objects. I did the following this morning when sitting on the terrace, where we have some happy plants coming between the stone tiles:

My visual scene:

          +-------------------+
          |                   |
          |                   |
          |     V1            |
          |                   |
          |              V2   |
          |                   |
          |                   |
          |      V3           |
          |                   |
          +-------------------+

V - plants growing between cracks

Suddenly I experience straight lines between the points of interest in the visual scene:

          +-------------------+
          |                   |
          |                   |
          |     V1---X---V2   |
          |      |       /    |
          |      |     -/     |
          |      |   -/       |
          |      | -/         |
          |      V3           |
          |                   |
          +-------------------+


V - plants coming between cracks
X - the center of gaze
| - the feeling of the lines between the points

If I move my eyes between the plants, this effect is intensified, I think. And then I think, do I not feel like I know the distances between those things, as the lines between the points?

There are very ghostly faint (purely virtual so to say) lines between the objects.

Perhaps those lines are the potential eye movements, expressed at layer 5 of objects and 'object group' representing cell assemblies. That says 'Move your eyes like this, please'. Because they want to be looked at. This is useful information to the rest of the system because the rest of the system can say from this exact eye movement information what the distances are.

Isn't this part of why people of old have looked at the stars and saw the patterns, the constellations?

If the game of the circuits is for some reason laid out in a way that forces many eye movements, it would be useful for the memes to output potential movement trajectories with multiple stops. I.e. if we for instance build in a restlessness in the circuits, the memetic landscape changes. Because every meme would like to say look to me and then never away. But if we force moving around, then the memes play the game of move to me, then move to this associated thing, which will lead you back to me.

If the brain uses the output of these trajectories to pattern complete some prediction states from this, i.e. move 5 degrees to the left and you see the banana, move 10 degrees to the top and you see the apple. And so forth, then the relationship, the where is encoded in this composition of possible movement and prediction.

Something else that falls out of the model:

If object A says, move to me!, it would also like to output /move to A1, move to A2, … / where A1, A2, … are sub-points of interest inside A.

It is impossible for me to come up with this idea without having the layer 5 circuits in mind, quite humbling. Because who knows, maybe there are 5 other things like this?

A replicator is a piece of knowledge that knows how to replicate itself.

Dawkins [1976] established the notion of abstract replicator theory; This is how he was able to predict computer viruses in the The Selfish Gene, in 1976.

The genes are only one kind of replicator, and the abstract theory of evolution applies to all replicators. Similar notion: universal Darwinism.

Turns out I am a lumper in this case. I would go so far as to say there is 1 kind of thing, an abstract knowledge container. That genes are only another kind of meme, too. It is some pattern that has a high frequency in the multiverse or something. A piece of knowledge that 'fits many pieces' of reality.

This is bound to leave out 80% of what the striatum is doing.

-—

Speculations On The Puzzle Of Tourette's, Musings On The Hypothesis Of Behaviour Encoded Situation Analysis

Givens:

• Input to the striatum is from 2 types of neurons in layer 5 of all cortex. [insert sources]

• Striatum is implicated with 'generating' 'movement patterns'

  Other expressions of emotional states, such as smiling, also originate in the striatum and pallidum, and we are very sensitive to the emotional states these movements represent. ‘Faking’ a smile with conscious movements uses cortical motor control instead, and produces a result that looks false to others in most cases.

  (here)

• Striatum makes modulatory, inhibitory inputs to thalamic relay nuclei [M. Sherman]

• Consequently, this doesn't look like the pathway carrying the information for how to move. It is much more parsimonious to consider the role of striatum as some kind of selector, filter, or orchestrator.

  (This is counter to the current mainstream thinking about striatum).

• Striatum by default inhibits thalamic relay nuclei (via globus pallidus int.)
• Parkinson's is a degeneration of Substantia Nigra Pars Compacta, which makes dopaminergic innervations to the dorsal striatum etc. Pars compacta

• Sleeping Sickness,

  The disease attacks the brain, leaving some victims in a statue-like condition, speechless and motionless

• Oliver Sacks in Awakenings (1973) talks about the sleeping sickness patients that were rejuvenated by L-DOPA. (Small documantary about this).
• But this had the effect that the patients had compulsive behaviors.

Speculations:

• Let's assume that layer 5 is the 'behavior output layer' for all Cortex.
• Further, layer 5 behavior streams are the latent space encoding of higher-order thalamic relay nuclei (see A Curious Arrangement on this page)
• There would be 'behavior streams' that simply participate in the information processing of analyzing the situation.
• This would only be useful if the layer 5 neurons that go to thalamic relay nuclei are correlated with the ones going to the striatum. [which I don't know whether that is the case or not. Considering that column activity is correlated, the question is whether those neurons are co-located in columns, and that sounds likely to me].
• Striatum is an element that listens to all "latent" behavior streams of Cortex.
• What exactly is relayed at those thalamic nuclei? The answer is part of the puzzle of the striatum.

But here is sort of one idea that makes some general sense:

Say that the striatum is a selector, it looks at all latent behavior streams being expressed at the cortex and takes a value judgment from some Darwinian wires [Braitenberg 1984, also called Darwinian Brain]. And removes the brake from the streams that get the most approval. Perhaps it is selecting only a tight subset or a single one. This would solve Minskies 'pulled by 2 spirits' challenge.

Consider an animal that is roughly equally thirsty and hungry, it stands just between a water hole and a pile of hay. By the time it walks over to the hay, the thirst dominates and it goes over to the water. But by the time it is at the water, hunger dominates. You probably want to select one behavior and stick with that for a while.

     what striatum sees:


                     /----------------------|
                  /--                     /-|
                /-                     /--  |
             /--                     /-     |
          /--                     /--       |
        /-                      /-          |
      +----------------------+--            |
      |                      |             /+
      |  +---+       +-----+ |           /-
      |  |   |       |     | |         /-
      |  +---+       +-----+ |        /
      |        +---+         |      /-   time flow
      |        |   |         |    /-
      |        +--++         |  /-
      |           |          |/-
      +-----------+----------/
                  |
                  |
                  |
                  |
                  behavior streams


----


                            cortex II
                       +--------------+
                       |              |
                       |              |
        cortex I       +----------+---+
     +---------------+ |          |XX |   layer 5
     |               | +----------+-+-+
     |               |            | |
     +----------+----+            v |      /----+-
     |          | XX |              +->  /-   /-|
     +----------+-+-++                 /-   /-  |  (pretend striatum takes all inputs into account
                  | |                +-----/    |   at once for simplicity right now)
                  | |                |     |    |
                  | +------------->  |     |    |
                  |                  |     |  /-
                  v                  |     | /     |
            +-----------+            +-----+/      |
            |           |             striatum     |
            |         1 |                          |
            +-----------+                          |
            Thalamus, relay nucleus    |-----------+ inhibition via GPi

            +-----------+                          |
            |           |  |-----------------------+
            |         2 |                      |
            +-----------+  |-------------------+   |
                                                   |
            +-----------+                          | n per nucleus?
            |           |  |-----------------------+
            |         3 |                          |
            +-----------+  |-----------------------+ n?
                                        |
                ...                     |
                                        |
             n-relay nuclei       behavior orchestration organization
                                  perhaps 'the piano of behavior'


XX - active layer 5 neurons, correlated, with targets in the thalamus and striatum

In this model:

• GPi is like an array of gas pedals that are 'off' by default, 'the piano of behavior'.
• The striatum is removing the brakes, orchestrating what behavior is expressed.
• The striatum is a brain, concerned with selecting, switching, and committing to behaviors.
• It would make sense if this selection takes a Darwinian value judgment into account.

    +--------------+
    |      +---+   |
    |      |   |   |   possible behavior streams
    | +-+  +-+-+   |
    | +++    |     |
    +--+-----+-----+
       |     |
       |     |
       |     +---------                               +---------| THA 1
              commitment assemblies?  --------------| +---------  THA 2
                                                      +---------| THA 3
            'commitment organisation' ?               +---------
                                             behavior orchestration
                 ^
                 | value judgment  ^
                 |                 |
     +--------+  |                 |
     |        |  |                 | dopamine ++
     |    D   +--+                 |
     |        |                    |
     +--------+                    |
                               [VTA?, SN ]

D - Darwinian Brain
THA - Thalamic targets
VTA - Ventral tegmental area
SN - Substantia Nigra

Note the failure modes of this arrangement:

• Switching commitment too much: Erratic behavior?
• Switching commitment too little: Failing to multi-task?
• Committing too little: Parkinson's? Sleeping sickness? Catatonia? Basal Dementia?
• Committing too much: Overeating, violence
• Selection too permissive: Ticks, Tourette's?

Why the tremor of Parkinson's? Perhaps something tries to offset the lack of commitment?

Why swearing and ticks with Tourette's? In the model, the striatum is too permissive. (But the striatum is not encoding behavior information, it only selects). The logical conclusion is that there are behavior streams that output 'swearing behavior', virtually constantly.

But why is this useful for the information processing the brain is doing? Who says 'Thank you this is the kind of information I needed to do my processing'?

Hypothesizing:

• Swearing is a more primitive form of language (see Pinker 2007).
• From biological cybernetics: When wondering how to build language into Braitenberg vehicles. It is a useful first step, to make vehicles make sounds for the situations they are in.
• (semi-wild, but parsimonous I think) An information stream representing the notion 'I am dissatisfied', could be encoded in terms of swearing behavior.
• With the bonus hunch that the insular/cingulate cortex would represent dissatisfaction (hedonistically, socially,…), and so forth.
• If the striatum is too permissive, it could accidentally commit to expressing this behavior.

Not because the person wants to swear, but because part of how we analyze situations includes an encoding of dissatisfaction, virtually always.

I believe this dissatisfaction is a deeply animal aspect of our psyche. It puts us as an animal in our situation. It is simply part of the whole arrangement. Part of what gives it its flavor is hooks for memory lines and so forth.

Why ticks?

Memetics says there exists a meme, which is partly made from the tick behavior. If the striatum is too permissive for some reason, the memetic landscape is changed. A meme can strategize to span across striatum and express behavior which will, in turn, activate its activators, and so forth in a happy behavior loop.

This shows us that the healthy memetic landscape probably has a bias to not make the same behavior over and over and things like that.

You don't know how you remember something - and you don't want to know.

Let's consider an alternative to the action-hero meme thought experiment for a moment. Imagine we implement a cognition machine software, where the fundamental data structure is a 'narrative unit'

+-------------------+
|                   |
+-------------------+
   narrative unit

In order to remember something, the cognitive machine can pull a move like this:

 (concat
     I-start-remembering-something
     random-noise
    (mix I-know-what-I-remembered random-noise))


 output:

 +----+--------+-----+  narrative unit vague
 |XXXX|OOOOOOOO|ROROR|
 +----+--------+-----+


X - concrete "I start remembering"
O - unspecified random noise
RO - Semi specified "I remembered"

This narrative unit doesn't have much substance you might think. Isn't there so much random noise?

Vague states are great templates, they are context to the meme-machine.

So you are some competent meme that knows if there is a situation 'I start remembering'. Then you can go and activate some other dude that will provide some half-baked memories to the system, and so forth. You don't know how it works either, but you know some buttons you can try in order to make your cell assemblies go in harmony with the overall situation. And the overall situation is that the cognitive user is remembering something right now.

Perhaps the thought pumps will not settle until some satisfying connectivity is reached in the memes. (some details are still left out from my story here).

But something happens that makes the narrative unit go in harmony with the rest of the network and tada - you have a filled in memory:

 run meme-machine,

 output:

 +----+--------+-----+  narrative unit concrete
 |XXXX|CCCCCCCC|RRRRR|
 +----+--------+-----+

X - "I start remembering" (concrete)
C - filled in memory states (concrete)
R - 'I remembered' (concrete)

Funnily enough, I think that is the one you remember afterward, too.

So the cognitive user will only ever see this thing working crisply and clearly.⁴⁸

—

What happens when it doesn't work? Here is a pet hypothesis on tip-of-the-tounge from this perspective:

 run meme-machine,

 output:

 +----+--------+-----+  narrative unit - very concrete
 |XXXX|OOOMMOOM|RTRTT|
 +----+--------+-----+

X  - concrete, "I start remembering"
OM - mix of vague states and failed attempts
RT  - 'I remembered', 'I speak',  concrete (prediction)

The 'I remembered' part never really goes into harmony with the rest of the narrative unit, because the meme machine failed to deliver the memory details. You start having very concrete story snippets about failed attempts and 'search process' information states. They all didn't help. Leaving you very unsatisfied. The release of a 'good idea that fits well' (see More Cell Assemblies Thoughts) is missing - presumably creating a frustrating feeling.

I think what happens now is that the machine usually also has some tricks for delivering. Perhaps make the whole narrative unit (or situation) more clear/salient/important/central in the mind. Presumably, you do this by activating all the cell assemblies with high frequency, and increasing the threshold so other memes are drowned out. (this is not clear to me yet and very interesting topic of thought-mechanisms). (Such things are labeled extremely broadly as 'attention' in cognitive neuroscience).

Or perhaps you take the narrative unit states, put them into the thalamus and make it the inputs to the rest of the mind, but I'm not sure yet. Perhaps that gives you a fresh perspective on things.

Either way, you sort of sharpen everything in the unit, including the RT part. That is the part that is right now 'in the future', where you have remembered and you speak, using your tongue muscles. Since you activate the whole thing you activate all the memes strongly here - including the memes that have to do with moving your tongue. You want this story to work out, in a high level of detail.

Perhaps this feeling also tells us something about how speech is represented in the mind in the first place. Something that has to do with moving the tongue, that is for sure. And meaning feels like it can 'sit on the tongue'. It's funny that it is the tip of the tongue.

—

The bottom line is with vague narrative units you can build a lot of cognition. (If you have a nice meme machine at hand, too).

The fundamental notion of the cell assemblies is 'pattern complete'. We can get the whole out of a part. This is useful, it makes vague ideas meaningful.

Perhaps then 'confabulation', 'filling the blanks', 'everything a little bit until something fits', and 'auto-completing to success' can be the fundamental operation of a cognition machine.

This is simply to wonder how far you get with this main notion in mind. (See The Software Engineering Approach. You need to consider what the building material you have in mind can already do in a wider context. So that you can see what it cannot do yet or where it doesn't fit at all.)

—

'Harmony' here means what kinds of activations are supported by the network. These could be described as the 'resonant' modes of the system.

Braitenberg had the idea to call these 'conflagrant modes' maybe. To separate it from physics.

—

I wrote this before thinking about some more derived topics on this page.

Contrast And Alternative gives a mechanism of how the system 'stops thinking'. The cell assemblies that manage to ignite in each neuronal area can inhibit their alternatives via the thalamic input organization. Thereby making the system stop thinking.

A signal that 'keep thinking' is required might come from Socratic wires, which can represent the absence of a 'fitting' interpretation. Or perhaps the absence of some 'fitting' situation makes the thought pump oscillate.

Also, all Cortex activity is input to the thalamus already. It would not be a special move, but it is the default architecture.

1. Imagine some early cortex, simply representing some olfaction data. [Braitenberg 1977 mentions this, I guess he wasn't wrong. Also makes sense given that the olfaction cortex is the least derived cortex.]
2. Now make some neurons extra sluggish, or make some Autapses (neuron simply activates itself),
   • make some slow neurons that store activity patterns longer than the other neurons.
3. There is an evolutionary driver for 'memory' in biological cybernetics. Memory is fundamental to computation. Biologically intuitive: Memory must be one of the main reasons to have brains in the first place.
4. If you have some slow neurons, the cell assemblies spreading into the slow neurons will live across longer periods. This way we have something in between short-term and mid-term memory.
5. It makes sense we have evolutionary drivers for mid-term memory. For instance: Make a population of neurons, add things from 2.
6. You have a problem now: The activity of staying around is great, but always remembering everything is useless. I.e. I don't want to remember constantly the banana I looked at 1h ago. So another driver to isolate the slow neurons.
7. Now you have an evolutionary driver for storing and retrieving memories from that slow place.
   • A load/retrieve organization.

You have another reason to isolate the slow place, that is epilepsy from all the activity.

The hippocampus has been called a flash drive. I will just take over this nomenclature and call this hypothetical slow place the flash drive (area) implementation.

In some cases there may be a stellate cell, a so-called interneuron, interposed between the sensory fiber and the pyramidal cell, perhaps for the purpose of switching an excitatory sig- nal to an inhibitory one. This is in accordance with the observation that small stellate cells populate particularly the sensory areas of the cortex and are especially frequent at the layer of the cortex, the so- called fourth layer, where the thalamic afferent fibers terminate.

Braitenberg 1977.

[wip]

See also

• Unsupervised Learning (ML)
• Predictive Coding

 [P]----+
  ^     |
  |     |
  |     v
  |    p-state
  |      |
  |      +------[C] Compare the last prediction with the new input
  |      |
  +----sensor-state [inputs-brain]
          ^
sensors---+

Different design decisions can be made here.

In general, the idea would be that you keep around a representation of what your predictor [P] says, then you compare [C] the incoming sensory data with the prediction.

If the prediction is good, fine everything moves forward. (or decide here to reward [P]).

If the prediction is off, you can immediately make a surprise signal, and make the system be about the inputs.

[inputs-brain] is something that merely represents what the sensors say. Everything looks like the thalamus is the place where this happens in the brain.

Further, you can have evolutionarily drivers to make this the ultimate input nucleus, after all, you can copy all 'Input' machinery. But this time the input is some other pieces of the brain, internal inputs then.

You can hook that up to a Darwinian brain, too. Which makes value judgments on what kinds of inputs are interesting and so forth.

The basic move is to treat the predictor as a black box, you reward it if it does what you want. You, the smart Darwinian wires, never know how this thing works. But it helps with survival and procreation for some reason.

Basic mechanisms for rewarding memes/cell assemblies:

1. Make them active longer (via plasticity they will entrench in your network and will stay around).
2. Make them active at a higher frequency (same thing, plasticity rules).

+--------------------------+
| X            X           | predictor / thinking goo
| |     X      |           |
+-+-----+------+-----------+
  |     |      |
--+-----+------+-----------------X-------------   j-lines
--------+------+-----------------+-----
---------------------------------X-------------   [joy maker nucleus]
                                 |
                                 |
                                 |
                                 |
                               Darwinian approval [ yes / now]

Here is a simple reward scheme you could make for cell assemblies.

1. Make some random wires through the thinking goo, call them j-lines for "joy lines".
2. Joy maker nucleus selects a random subset of j-lines, call it j-mix, and activates. This will morph the memetic landscape of the thinking goo. Intuitively - if there are 2 roughly equally likely cell assemblies F and K, then if there is a random j-line going though K, but not F, it will be more stable and win.
3. Now you can run your usual cognition machine (for instance in the simplest case a sensor-predictor loop).
4. From a Darwinian wire criterion you get 'worked well' or 'not good'.
5. If good, take the current j-mix and shoot activity up the wires. Presumably, you will reward all the cell assemblies still in short-term memory. (I.e. the cell assemblies your j-mix is activating right now will stay around more, because of our plasticity rules). Perhaps you could also feed the situation / sensory inputs that lead up to the approval, to get a richer memetic network. -> This is of course allowed to become arbitrarily complicated. -> Eventually, the memes in the thinking goo will become capable enough so they try to hack the Darwinian appproval for joy. -> That's a fundamental problem a meme engine has to deal with.

More generically, I call such a mechanism perspective-lines. Because of the way they change the memetic landscape (dynamically). This is almost the same as the so-called 'attention' of current ANNs. Abstractly, it is changing a meaning-landscape into a different shape. 'Perspective mechanism' would have been the less confusing label. Whatever.

This is probably wrong, maybe too complicated.

Consider something very similar to the above:

+--------------------------+
| X            X           | predictor / thinking goo
| |     X      |           |
+-+-----+------+-----------+
  |     |      |
--+-----+------+-------------------------------     perspective-lines
--------+------+------------------------
------------------------------------------------    [ dream-nucleus ]

Let's say the system goes into dream mode, where it doesn't need to perform prediction/perception on the fly all the time.

There are many things you might be doing during dreaming. It is a basic move of explaining biological intelligence to consider that 1/3 of everything this thing does is sleep - so you can put as many mechanisms into a sleep mode as you put into a performing mode.

Here is a meme-pruning algorithm (just an idea):

The main idea is that you can try a subset of your thinking goo⁴⁹. If it manages to predict the situation, then reward the smallest subset that manages to predict - automatically punishing selfish memes that don't contribute to the prediction/ success.

1. In dream mode (no or low sensory inputs, no pressure to perform cognition).
2. Select a random subset of perspective-lines p-mix, activate that subset.
3. Select a known working sensor->predition pair (allowed to be more complicated).
4. Feed the sensor states into thinking goo. (For the sake of argument, your p-mix and the sensor state you feed are the only sources of activation in the system now).

5. Run your usual cognition,

   If the predictor comes with acceptable predictions, then reward the current p-mix. (Shoot activation up the wires). This way you punish all selfish memes that didn't contribute to the prediction capability of the thinking-goo. And you will get the kinds of memes that predict real things about the world.

6. If the predictor is not capable of having a satisfactory prediction for your input states, either select a new p-mix, or augment the the current p-mix to p-mix-2 by adding more p-lines. Then continue with 5 until you get results.

Open questions:

• Would you feed high-level prediction states and see what the continuations are?
• Or would you be able to feed sensor data? It doesn't look like thalamic input nuclei like LGN are on during dreaming, speaking against this idea⁵⁰

Reasonings:

• This fits well with the anecdotal and experimental observation that dreams from early and later in the night have a different character.
• Incidentally, it seems like the earlier dreams have less cognition 'on'.
• The dreams later in the night then, have a self and language and situations and so forth. This fits with the idea that whatever is happening, maybe it is trying out subsets of cognition.

—

Maybe this can be simplified into 'try out subsets of your cognition' and simply have the rest of the machine reward memes like usual. You get the same benefit - rewarding 'sufficient' memes, pruning away the free-rider useless memes.

The general view on what the cortex is doing for an animal:

It represents an ever wider analysis of the situation and affordances; This is only useful when it is about survival.

In broad terms, there are 2 elements in the system:

+----------------------------------+
|                                  |
|  ^                               | thinking goo (cortex)
+--+-------------------------------+ (makes memes possible)
   |                                 (large network, many possible meanings)
   |                                 (made from stupid stuff)
   |
   |
 --------------------  [Darwinian Wires]
 ------------
 organize, drive, extinguish activity - shape the memetic environment
 reward the thinking-goo when it is useful for survival

                     (not many possible meanings, pre-allocated meanings)
                     (precise and competent, implements math when it needs to)
                     (has the intelligence of evolution inside it, is highly engineered)

The thinking goo is the ultimate Baldwin space, that must be shaped in a late binding.

This doesn't have sharp delineation, like much in biology. So when you look at the cortex you still see this or that wire and this or that architecture shaped by Darwin's hand. We can expect that wherever evolution could help out, for instance with development plans, it did so.

Still, I submit the fundamental dynamic nature of such a navigating device lies at hand.

The basic problem the 'rest of the brain' has to solve is to make the thinking goo be about survival and success.

My view is that there are 'smart Darwinian wires' that come from evolution, that are designed to make a thinking-goo into a good thinking-goo. That probably means things like rewarding and selecting memes, making memetic drivers in the system for success somehow and so forth.

One job of the computational cybernetician then is to describe and fabricate such memetic landscape machine tricks on one hand. (Similar to the ones that are implemented by the Darwinian Wires in the brain).

And on the other hand to make a computational model of a thinking-goo. It is not required, possible or desirable to get all the details exactly right the way the human brain does. But it is very useful to have a working implementation, if just for the benefit of knowing that it can work.

Note also that we don't care about the contents of the thinking-goo per se. We care about the kinds of content that it should support, so we can build a resourceful implementation of thinking-goo. (This is roughly what it means to have a dynamic computer program. 'Data is the ultimate late binding'. And so with the brain, the network and its content are the ultimate late binding).

In a further step, we can provide machine intelligence with the power of the computer. This is an edge that is not available to biological intelligences, that don't have the benefit of running on a computer. Such things are on the topic of intelligence mechanisms, which I would say are one level above the main concerns of a model of cognition. Roughly speaking, intelligence is the realm of stuff that a cognitive user knows herself; While cognition is the realm of stuff that the cognitive user has no idea how they work.

I don't have answers yet.

It fell out of favor for a while to talk about lateralization, because popular science was taking it as a beautiful story of 'your other side knows different things' Bla Bla.

I think it is now that more nuance comes back into our conceptualizations, And there is a rich puzzle of of lateralization after all.

Givens / Reasoning:

Givens:

1. Long-term memory, mid-term memory, and short-term memory are different systems [cognitive neuroscience]
2. Consider patient HM. Without hippocampus (+ close by structures) mid-term memory is gone, with anterograde amnesia, but long-term memories (up to a few years before surgery) are intact. Short-term memory is intact, too.

—

Step 0: open one mind to receiving mid-term memories. I.e. activate the situation of I remember. I.e. activate cell assemblies that support the idea of remembering vaguely.

Step 1: Reinstantiate cortex activity more or less globally*, basically giving some context. You need a hippocampus, or at least a related temporal lobe to pull this off [see patients HM and EP].

Let's speculate: what comes from the hippocampus is quite course-grained, then:

Step 2: Fill the low-detail information 'scaffold' that came back from the hippocampus with life. Do this by using the cortex, its thinking, its imagination and so forth to mentally enact the memory. It's my hunch that this process even makes you voice act the people in your memories.

I think all steps 0-2 are neuroscientifically plausible, and fit with a high-dimensional computing framework like the cell assemblies.

Remembering in this notion is separated from the memory. Note that this applies only to mid-term memory. Other kinds of memory are virtually guaranteed to have different kinds of mechanisms.

Remembering is the process that creates cognition states/ imagination states. And presumably, that happens in Cortex. The Memory is allowed to be stored in the hippocampus, frozen for all we care right now.

The brain's function is to do computation - well what computation? It is like saying The car is doing acceleration, or the tree leaves are doing biochemistry.

In my view, it is in terms of software that you understand a computation.

When somebody looks at some neurons and decodes information out of the activity this way I can tell whether the animal is thinking left or right. Without a software-level computational theory of the brain, you don't know whether you are looking at a conveyor belt of information, some intermediate outputs of a calculation, or a representation of the outcome of some calculation. If you can decode information with great quality, it is even more likely that you are looking at some kind of message sender, where the message represents the outcome of a calculation.

I simply ask myself would you be able to distinguish a conveyor belt from a machine?. If you can't, then your model and theory are not computational, but some kind of substrate analysis (that is not useless, but it is not what will explain brain-software).

These kinds of nuances are lost, if computation is synonymous with the function of the brain. The only way the structure and function of the brain will be understood is in terms of the mechanisms of this computation, not the label computation.

My slogan way of expressing this problem is you can replace computation with wizardry in a neuroscience textbook and you get the same information content from that book.

Clearly, the function of the cerebellum is to make the wizardry necessary for error-correcting motor movements. It has these and such sensorimotor afferents in order to achieve its wizardry.

It's like saying the water flows through the xylem to the leaves, in order for the leaves to do it's biochemistry. This is ok and useful, but it is not a biochemical plane of explanation.

They should just say magic or wizardry instead. That removes the confusion from the picture.

I mean it is ok if you do brain-science and substrate analysis and talk about what kinds of things are connected to where and so forth.

Exploring and communicating the properties and nature of high-dimensional computing is a fundamental aspect of explaining the cortex.

Darwin did not need to be smart in order to make a useful thinking substrate.

We know from insect neuronal plans and so forth very detailed math and models what some circuits are doing, for instance fly motion.

The 'smart wires world', Vehicle 3. Those are wires that are shaped by evolution, therefore the meaning is allowed to be very precise. It means 'you move when x is true' and so forth. It also implements some complicated math: subtractive inhibition, divisive inhibition and so forth. If one of these neurons or wires is gone, that is a big deal.

The smart wires world stands in contrast to the ultimate Baldwian substrate (cortex). Welcome to a world where the wires are stupid and don't matter, but from the many stupid ones you get something useful.

I submit that the cortex looks like it implements some kind of high-dimensional computing [Kanerva].⁵¹ Therefore in some ways, everything is still the same, evolution makes wires that make computation. However since the computation is of a certain flair, there are completely different requirements for the implementation.

The wires in thinking goo (high dimensional computing device) are allowed to be random, in fact, they are only useful because they are random.

The nature of this computation is like alien tech compared to the computers that we know.

As a biologist, I had the feature detectors in mind and thought about those neurons being connected. And of course, then you say it is so dauntingly big. This is when you look at the trees but you should look at the forest.

Because of the nature of high-dimensional computing, you can see the thinking-goo as a kind of information representation substance. A piece of cheese has temperature, you don't need to worry about the atoms. So too, a piece of the cortex has information states, you don't need to worry about the neurons.

How this works is a computer science topic. But trust me it works. Check Santhosh Vempala explaining assembly calculus.

—

This is the source of confusion when you hear that this or that aspect of the cortex is innate. How does that fit with the data of the ferret re-wiring experiment? You get orientation columns in the auditory cortex.

The confusion comes from the fact that both things are true. The substrate is random and it starts representing its inputs more or less immediately.⁵²

It is the ultimate dynamic late-binding substrate (to use software engineering terms).

—

Perhaps the reason the cortex evolved from a tertiary olfaction ganglion is not so surprising in light of the high dimensionality. Perhaps it is the nature of information processing of olfaction, where you have 50 sensors, not 3 like in vision. Perhaps this is the fundamental nudge towards the concept of 'mixing information' that is perhaps the most primordial aspect of the cortex then.

—

Similarly, you look at neurons firing and you say here is a Jennifer Aniston neuron. The network seems so smart, but it cannot work with single neurons either because:

1. If you put concepts into single neurons, you don't have enough neurons for the amount of concepts.
2. Neurons die, and that does not seem to disturb the system much.

The high-dimensionality computation perspective flip is satisfying:

Concepts and symbols can be represented by randomly allocated subsets of neurons, that temporarily ignite each other as cell assemblies. It doesn't need to be the same neurons tomorrow and it probably isn't the exact neurons tomorrow.

The neurons are important and unimportant at the same time.

You can take a cell assembly and lower the eagerness to a small center of 'important' neurons. They are important right now but they are important in a dynamic way.

If those neurons had not been there, there would have been some others. And they would have represented the concept of your grandmother just as well.

From this, I have now grown a view of brain functioning that centers around the idea that the brain is an 'activity management device'.

You gate the activity coming in (Thalamus), you store some activity at a slow pace (Hippocampus), and you ignite activity internally (striatum?). And things like this.

If I would know that thee TRN was segmented, then I would go like holy shit that fits.

This would fit with a circuit/mechanism where the thalamus would have slaps of roughly 1/2 * 10⁵ neurons, each making wires to the cortex, going through a segment of TRN, with 1/2 * 10⁵ wires going through it. Half, because the thalamus is a unilateral concept (1 hemisphere).

This would fit, if you would want to gate 1 holographic encoding at a time. Because at first glance it would be a bit strange to ever separate one encoding out. (This consideration depends on the idea that TRN is a kind of activation gating 'attention' mask around the thalamus).

This is just 1 of many things that could be true though. Perhaps the Thalamus isn't encoding a context at all. And if it does, then maybe it is useful to have 'partial attention', after all. (The brain would be more complicated).

—

If the thalamus would represent the cortex context in this way, you might expect that there is a hippocampus -> [thalamus-HD-encoder-nucleus] -> cortex circuit; And the basic midterm memory retrieval mechanism would be to reinstantiate the cortex context. (See below musings on hippocampus, future).

'thalamus-HD-encoder-nucleus' should be roughly 1/2 * 10⁵ neurons large. It would be one of the Anterior nuclei, then. If the simple 1:1 reasoning of 'what is connected to what' can be applied here. (Which is not clear).

Or, there are slaps of 1/2 * 10⁵ neurons in such a nucleus. Where 1 slap would correspond to 1 context context.

—

Intuitively, this might be incredibly sparse somehow. Let's say that a tiny subset of compartments active is already meaningful. Speaking against the idea that you would need to encode all 1/2 * 10⁵ neurons.

It evolved as a tertiary olfaction ganglion [Braitenberg, 1977].

One might consider the perspective that all of cognition is ever more fine-grained and ever more abstracted sniffing.

There is more time between me and my problem in the past.

This requires

1. Some memory capacity
2. Sniffing out the essentials of a situation

   [ ate toxin ]  <--   me
|                 time            |
|                                 |
+------------------------------+--+
                               |
     [ relavant-sitution ]  <--+  situation sniffer

There is an obvious evolutionary driver to increase my sniffing capacity here.

 me -->  [ goal ]
   time


 me -->  [ problem ]
   time

Evolutionary driver to make the time arrow longer.

If I can sniff out the relevant situations in advance, that is useful. If I can put more time between myself and my goals and problems, that is useful.

There is a use for a situation sniffer, with an evolutionary driver. Towards being able to think further ahead, make plans, have a feel for a situation, have a feel for a situation in longer timescales etc. etc.

—

Consider a tertiary olfaction ganglion. It might start evolving some more neuronal tissue, to be able to keep more memory states around, and to then be able to have more derived states. For instance in higher abstraction hierarchies or higher temporal domains.

—

One of the things that a theory of the cortex is doing is talking about how it is useful to have more neuronal tissue.

If I can sniff out a problem before I have it, I am putting time between me and my problem.

If I encounter a problem and I can keep track of what happened in the past, I can put more time between me and the problem.

When we go into the cognitive realm (like tree shrews, primates, and dolphins?…) there is enough the machinery of the system so that we can talk not only about the time between and my problem but inferential steps between me and my problem.

Thank for the hypervectors, (Carin Meier, Kanerva and friends). The hypervisors are such a beautiful building material. Able to mix and morph and be vague and about themselves and have directions. All kinds of things that I feel are important for building material of cognition.

The bundle is like the `mnemotrix`.

Rotate + bundle is like `ergotrix`.

Bind is maybe like a synchronous activity. It is I feel freshly derived signals from 2 signals.

Of course, since the brain is a messy information mixer, firstly it does all of them at the same time. (Except preserving the bind for important information?). And secondly doing them all at the same time is good design.

Hunch: if you have static, timeless cell assemblies, their function can be abstracted with a hypervector.

So I would like an assembly->hypvervector projection.

Hunch: maybe some frequencies of activation are there to hold ideas in the mind, without modifying them so much. There is certainly use in the system for timeless ideas. (But the brain needs to make activity back and forth, so it can only slow down time at best).

But we, thinking about the abstract machine that we can run on a computer, have the freedom to freeze ideas.

—

… ideas:

To find a hypervector representation of a cell assembly A (the symbol you might say):

1. Find a kernel of neurons that can re-ignite a cell assembly A (meaning condenser…?)
2. Look at an arbitrary abstract space of neuronal area (with geometry, you could imagine a straight line) (of size h-dim). You need to remember this space though, call it a hyper-line.
3. Project into a hypervector (trivial), say which neurons are active 1, inactive -1: [ -1, 1, -1, … ]

hyper-line is in effect a projection map between hyper dimensions and cell assembly neurons.

(hyper-line neuron-idx) -> hyper-dimension

In order to go back from hypervector space to cell assemblies:

1. Given a hyper vector and a hyper-line
2. activate the neurons with a 1 in the hypervec, (optionally inhibit the ones with a -1).
3. This re-ignites the cell assembly, or at least biases the system to do so.

—

So in order for this to work nicely, you can now do the math, depending on the dimensionality of the vectors you want to use. -> this is 1:1 to the space of neurons that map.

—

hm not sure yet, tried to have some ideas below. Doesn't flesh out yet:

—

I don't need to calculate the neuronal net at each time step if the function of the neuronal net was timeless.

Idea: from a neuronal area, you can make neuronal units a hyper-line mechanism.

It would be kind of cool if you would get this with pure assembly calculus.

This here is kinda of the first idea:

                                   |
    nueronal area                  |
                         [ A ]     o ..
 +--------------------+            |
 |                    |            |
-+---X--------X----X--+------------o 1 hyper-line (part of the cell assembly A)
 |                    |            |
 |                    |            |
 |                    |            |
 +--------------------+            o 2
                                   |
                                   |
                                 hyper-hyper-line etc.
                                 second-order-hyper-line
                                 hyper-2

You can implement hyperline with cell assemblies/neurons, I am sure.

It seems tempting to attribute some of such function to the straight axonal collaterals of pyramidal cells through the b-system.

Say we have an assembly A active, now we can find a hyper-line or set of hyper-lines that are well suited to re-ignite the assembly A.

As programmers, we have a strong urge to get all the abstraction levels straight.

The program runs on our brain, the thing that makes things like perception, memory, working memory, language, higher reasoning, social reasoning, feeling and so forth. Is a program that is implemented by the brain, but not in terms of neuronal activation or anything.

But in terms of its fundamental data structures and so forth.

One way to get the abstraction barrier between the 2 levels right is to have 2 implementations in mind.

I have so far 2 or 3 ideas of implementations, against which somebody might try to program a cognition machine.

One is cell assemblies, one is vector symbolic computing, and one is imaginary magic books in a vast library.

Consider motion detectors: They must represent their meaning in terms of the data structures of the cognition machine. They cannot be little stacks of motion movies, they must speak the language of the cognition states.

It's a bit like the transistors of your computer fire with electricity, yes, and you can imagine the memes flowing and so forth. But in order for you to be able to delete a file on the disk, it must be represented as a structure in the operating system.

—

Gregorian Cognition:⁵⁴

There is maybe roughly 1 other abstraction level in a cognitive machine, that is when you start having a sophisticated user of the the rest of cognition. Making things like working memory, higher goals, structured thought and so forth. It is quite striking that the prefrontal cortex seems to be doing these higher executive functions, and that the pre-frontal is biggest in humans compared to other mammals.

This is then firmly the layer of abstraction that we know ourselves. I know how I use my working memory for instance by 'keeping the information in mind', for a while or by repeating a piece of language or visuals in the 'mental scratch pad'.⁵⁵

These are internal muscles of a kind that allow me to use the rest of the cognition machine to bring to mind a piece of information etc.

This mental scratch pad is available to me as a user. What is not available is one level below - what I call the Cognition Level. That is how perception works, how memory works etc. I have no idea how it works. For instance, I just experience my visual field and that's it. There is approximately zero insight, by the user, into where this visual field comes from.

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As an aside on AI history:

It is kinda of a cliche story of AI, that the people tried to put these highly derived symbol thought things into computers. The narrative then usually goes something like 'and then we figured out that you need something messier than symbols' or something.

I think the real story is quite a bit more nuanced, with the dynamic programming Lisp people talking about programs that modify themself, and build societies of small, contributing agents (who have competence without comprehension, see Minsky 2006).

Not to forget there is the whole branch of 'biological cybernetics' that could have been, but wasn't, the basis of AI and computer science. See Heinz Von Foerster etc. They built the first 'parallel computer' in the 70's or something; Completely different paradigm of computing based on McChulloch-Pitts neurons. If we had more of these ideas influencing computer science, who knows what kind of alternative thinking substances we would already have?

• https://www.pnas.org/doi/abs/10.1073/pnas.1406077111?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed