Wednesday, March 9, 2011

Proposal for Directed/Guided Evolution of a Cognitive Module/Algorithm by Step-by-Step Modification of a Basic One - from Archicortex to Neocortex

This is a direction I realized last year during a discussion on Boris' knols and mentioned there, but later I shortened the comment there, because it wasn't the appropriate place for the details

The idea is about designing cognitive algorithm achieving properties that Boris proposes, however grounding it and deriving it on a supposedly simpler and easier to understand cognitive algorithm that has existed before in lower species and was slightly modified by evolution.

Keywords: embryology, comparative neurobiology, embryogenesis, vertebrates brain evolution, cognitive module, cognitive algorithm, evolution, archicortex, neocortex, forebrain, hippocampus, mini-column, columnar organization, generalization, recording, prediction, scaling, differentiation, specialization

Premises

- Embryogenesis is selective segmentation and differentiation

In general, organisms are deveolped by selective segmentation (separation) and differentiation of cells, a sequence of activation of appropriate genes.

- Small quantity of germ cells divide to form bulky tissues/regions - initial complexity is much lower than final and there are interdependencies. Simple mathematical example is fractals .

One reason neocortex may have relatively similar columns all over might be because they might be building block of the cognitive algorithm. However another reason, in another POV is that DNA has just not enough capacity to code complex explicit circuitry to make them all specialized by directed growing. Even if it had the capacity in theory, it's questionable whether biological "technology" would be capable to connect it with the required precision, because organism parts "grow like branches of a tree" ("The Man and The Thinking Machine", T.A. 2001) .

Bottom line: there are "leaves" of the tree, and the complexity of the leaves is limited.

- Evolution steps in phylogeny are supposed to be very small, and genome development is chaotic in mathematical sense - a small difference in the initial state (DNA) may lead to (apparently) vast difference in the final state - fully developed body.

Apparently big differences in structure may be caused by very small and elegant, functionally purposeful changes inside.

- Some of the operations that a mutation may cause could be, besides formation of a new protein: be or result in something like the following:


- Copy a segment (a block) once more, i.e. initiating division once more cycle

- Connect to another segmentation module (especially in brains)


- Amphibian's and Reptilian's forebrain, their most evolved part - archicortex/neopalium - has 3 layers. In comparison, general mammalian and human's most evolved (the external) part - the neocortex has 6 layers*

- Evolution, especially in brain, is mostly building "add-ons" , "patches" and slight modification and then multiplication of components(?)

Triune theory of brain, the new is a layer above, the old is preserved. The new modules are connected back to the old ones and have to coordinate their operation, and new modules receive projection from the previous. I think this implies also, that the higher layer should be "smarter" (more complex/higher capacity memory/processing power) than the lower, allowing more complex behavior/adaptation - otherwise it would just copy the lower layer results.

Amphibian's and reptilian's brains had cortex lacking 6-layer columnar structure of mammals, it's 3-layer (I don't know a lot about its cytoarchitecture yet). I couldn't accept that archicortex lacks some sort of a modular design, somewhat similar to the columns; it makes no sense for the archicortex to have been a random jelly of neurons, because even basic behaviors such as finding lair and running for cover require integration of multimodal information and memory. I don't believe also that mini-columns had appeared from scratch in the higher mammals.

Recently a little support on this speculation appeared; regarding birds, though, a parallel line of evolution:

From: Our brains are more like birds' than we thought: "


"...A new study, however, by researchers at the University of California, San Diego School of Medicine finds that a comparable region in the brains of chickens concerned with analyzing auditory inputs is constructed similarly to that of mammals.

(...)


But this kind of thinking presented a serious problem for neurobiologists trying to figure out the evolutionary origins of the mammalian cortex, he said. Namely, where did all of that complex circuitry come from and when did it first evolve?

Karten's research supplies the beginnings of an answer: From an ancestor common to both mammals and birds that dates back at least 300 million years.

The new research has contemporary, practical import as well, said Karten. The similarity between mammalian and avian cortices adds support to the utility of birds as suitable animal models in diverse brain studies.

"Studies indicate that the computational microcircuits underlying complex behaviors are common to many vertebrates," Karten said. "This work supports the growing recognition of the stability of circuits during evolution and the role of the genome in producing stable patterns. The question may now shift from the origins of the mammalian cortex to asking about the changes that occur in the final patterning of the cortex during development.


- The function of the Archicortex (hippocampus) in mammals is declarative memory and navigation.

See some of my speculations on: April 24, 2010 - Learned or Innate? Nature or Nurture? Speculations of how a mind can grasp on its own: animate/inanimate objects, face recognition, language...
Hippocampus

- formation of long term memory
- navigation
- head direction cells
- spatial view cells
- place cells

At least several or even all of these can be generalized. Places and navigation go together. Places are long-term memories of static immovable inanimate objects (the agent has not experiences that these entities move).

Navigation, head-direction, spatial-view, place-cells - they all are a set of correlations found between motor and sensory information, and long-term memories, which are invoked by the ongoing motor and sensory patterns.

The static immovable inanimate objects (places) change - they translate/rotate etc. - most rapidly in a correlation with head direction (position) and head movements.

Navigation and spatial view are derived from all.

Boris Kazachenko's comment:

(...) Regarding hippocampus, it controls formation of all declarative (not long-term) memories, not just about places. Declarative means the ones that got transfered high enough into association cortices to be consciously accessible.
My personal guess is that hippocampus facilitates such transfer by associating memories with important locations [mapping] . You'll pay a lot more attention to something that happened in your bedroom then to the same thing that happened on the dark side of the moon. I call it "conditioning by spatial association". (...)


// There's a whole topic about hippocampus functions and its competition with neocortex, for now I plan to put it alone and link to this.

Reptiles don't have association cortices, though, yet pretty impressive behavior of lizzards could be seen, such as this curious iguana looking behind the mirror to see where the other one is, and eventually hitting the mirror - see at 5:33.




My guess about archicortex' contribution is discovery of means to:

- Archicortex maybe records exact memories and correlations between memories/compare for match between sequences of sensory patterns

There should be limitations of the length of sequences, part of it might be caused by size constraints - animals with archicortex only, lacking the higher layers* just have very small brains. (*Cingulate cortex and neocortex for mammals)

I'm not an expert in vertebrate embryology yet, but I guess a simple reason
why fish and reptiles with big body keep very small brains - like 3,6 m white shark with 35 g of brains should be that:

- Germ cells that give birth of brain tissue of fish and reptiles divide less, or/and these species lack some hormonal growing mechanisms that species with bigger brains have

Both are a sort of "scaling issues".

Too small a brain has insufficient cognitive resources. On the other hand, these brains maybe don't scale also, because they wouldn't/didn't work better if they are/were bigger.

- Assuming general intelligence is a capability for ever higher generalization, expressed in a cognitive hierarchy (see J.Hawkins, B. Kazachenko, T. Arnaudov) and mini-column is assumed to be the building block of this process in neocortex, there should be a plausible explanation of why and how this module was formed and why this function gets successful

My functional explanation is the following:

- There already existed templates of circuits for exact recording, but they didn't scale
- The simplest form of generalization is recording at lower resolution than the input, and fuzzy comparison. It's partially inherited by the imprecise biology.
- Updated form of these circuits maybe added more divisions and cascade connections (and this may have started in cingulate cortex or higher reptiles, as well) which allowed for hierarchical scaling. Neocortex is assumed to have 6 layers, archicortex has 3. I'm not an expert in cytoarthitecture, and should check out cingulate cortex, but if there are no inter-stages between 3 and 6, this sounds suspicious for simple doubling somewhere during division and specialization. Or it could be several doubling operations.
- These new cascade connections allow for deeper hierarchy, scaling and multi-stage generalization. (Recording "exactly" alone is "generalization" and lossy, but without hierarchy which is deep enough this cannot go far - just for coping with basic noise.)
- There are mice with less than 1 g of brain which of course are much smarter than sharks (not to mention smart birds); however the advantage in micro-structure (mini-column) doesn't deny that mammalian brain scales in size and there is a correlation between brain size (cognitive reources) and intelligence, even though it's not a straight line. Spindle neurons, connecting directly distant regions in the neocortex are one of my guesses about why pure size might be not enough; another one is the area of the primary cortices, especially somatosensory (elephants, dolphins and whales have bigger brains than humans). See Boris' article about Spindle neurons and generalization: http://knol.google.com/k/cognitive-focus-generalist-vs-specialist-bias

- Neocortex does scale, but it's not surprising that it has constructive limitations as well as archicortex did.

- Classical and Operant conditioning, dopamine and temporal difference learning

It's quite a global feature of entire brain, maybe; but it has to be considered - classical evolving to Operant requires predictive processing.

Conclusion

1. Design a basic cognitive algorithm/module, scalable by biologically-like mechanisms, which allows reaching for, say, reptilian behavior.
2. Tune this basic module, multiply and connect intentionally to form a mechanism that "stacks" on a hierarchy, generalizes and scale the global cognitive capacity.

14 comments :

Boris Kazachenko said...

A few things:
- Layer I in neocortex is mostly lateral axons from deeper layers, with very few neurons. There goes your idea of “doubling”.
- Most brain areas consist of nuclei, - pyramidal cells surrounded by inhibitory interneurons, similar to minicolumns. Nothing new here.
- Shifting from necortex to archicortex is not going to help, you don’t understand how that works either. I don’t think it does “exact recording”, that’s too dumb even for a fruit fly (& no one fully understands how a fruit fly’s brain works either). Even if you fully understand how archicortex works, you still can’t brute-force evolution of a human-level neocortex out of it. That took ~100 million years, & biosphere had hardware you can’t even dream of.
- I constantly stress that my approach is strictly incremental. And I don’t start from neocortex, or archicortex, or a brainstem, - I am not a neuroscientist. My “incrementing” is in terms of functional complexity, which I fully formalize. I define the increments “manually”, & then try to understand how they could’ve evolved, & at what point such evolution can become fast enough to simulate. That does start from exact recording, then digitization, then comparison... Actually, even from entropy growth, in the grand scheme of things.
It’s unlikely that you’ll make a non-random advance through superficial dabbling, Todor. You can grow up & focus... or you can remain an artist.
Sorry for being negative, I do appreciate the attempt, & your quoting me. But, there’s work to be done.

Todor "Tosh" Arnaudov said...

Thanks for your comment, Boris!

>- Layer I in neocortex is mostly lateral axons from deeper layers, with very few neurons. There goes your idea of “doubling”.

The idea is about doubling of anything - simple tweaks in a basic cognitive module, leading to more complex and capable cognitive module.

>Most brain areas consist of nuclei, (...)

OK

>Shifting from necortex to archicortex is not going to help, you don’t understand how that works either. I don’t think it does “exact recording”, that’s too dumb even for a fruit fly (& no one fully understands how a fruit fly’s brain works either).


I don't agree on "dumb" and I rather want to understand a module that could lead to reptile level intelligence when multiplied, it probablu won't be exactly like archicortex.

>Even if you fully understand how archicortex works, you still can’t brute-force evolution of a human-level neocortex out of it. That took ~100 million years, & biosphere had hardware you can’t even dream of.

This idea is also about a functional model (a module) and that meaningful structural complexity might or should be much lower than the apparent one.

Modifications would consist of intentional and directed tweaks, supposed to lead to functional improvements.

This method is not pure brute-forcing and is supposed to be incremental, but with tests to see how it really goes.


>I constantly stress that my approach is strictly incremental. And I don’t start from neocortex, or archicortex, or a brainstem, - I am not a neuroscientist.

I think there should be clueues in comparative neurobiology and in the process of following the way of neural system development from somatic cells to neocortex, anyway.


> My “incrementing” is in terms of functional complexity, which I fully formalize. I define the increments “manually”, & then try to understand how they could’ve evolved, & at what point such evolution can become fast enough to simulate.
>That does start from exact recording, then digitization, then comparison... Actually, even from entropy growth, in the grand scheme of things.
>It’s unlikely that you’ll make a non-random advance through superficial dabbling, Todor. You can grow up & focus... or you can remain an artist.
>Sorry for being negative, I do appreciate the attempt, & your quoting me. But, there’s work to be done.


It will be done, constructive focus is a matter of time... I know, you've been hearing that. :)

Boris Kazachenko said...

You don't have a module. Exact recording is beyond dumb, a single neuron is smarter than that.
So, - doubling, tweaking, testing, - these words mean nothing.
Neuroscience can be suggestive, or distracting & confusing. The later is far more likely at your current state of knowledge.
You need to think in functional terms.

Todor "Tosh" Arnaudov said...

>So, - doubling, tweaking, testing, - these words mean nothing.

Right.

>You need to think in functional terms.

Agreed.

>You don't have a module.

Then I have to try to define a real one, to bite the problem at some point and meet real challenges. I had one with terminology you don't like, but have to be formalized for implementation.

Boris Kazachenko said...

Just want to add, these attempts to escape into testing & "evolution" are driven by ADD only.
It should be obvious that evolution is infinitely inferior to cognition, especially when it comes to understanding cognition itself.
This problem is meta-theoretical, those who can't do theory are wasting their time.

Todor "Tosh" Arnaudov said...

However it should be obvious as well, that this idea is not evolution the nature way or with random genetic algorithm, but by gradual modification of initial simple meaningful cognitive algorithm.

Testing helps to see is it work and to understand what you should optimize. "Evolution" is a lot into the heuristics that small functional changes may lead to vast final differences and the final module may be not quite different from the initial one.

Boris Kazachenko said...

And you do all that in your own mind. Because that's the best tool you have, till you get a working AGI.

Todor "Tosh" Arnaudov said...

I agree, however another meaning of this proposal (self-proposal) is: should start formalizing for real and laying it down in one form or another.

Drawing models as programs is a kind of formal definition and forcing yourself to think in functional, quantifiable and unambiguous terms. I think it may help a distracted one like me to engage and focus.

Programming is not supposed to be the essence of the work, the first level algorithm may start as a few lines of code. The virtual environment to feed and drive sensory inputs and motor outputs also could start very simple.

Boris Kazachenko said...

> the first level algorithm may start as a few lines of code. The virtual environment to feed and drive sensory inputs and motor outputs also could start very simple.

Short simulations won't tell you anything. Quik fixes won't scale, & potentially scalable algorithm won't produce anything interesting with simple inputs & low comput.capacity.

Todor "Tosh" Arnaudov said...

ОК... Then this will be confirmed by the lack of progress and the explorer will either give up the problem or become "narrow-ist" (not likely for me), or will have no other choice, but learning how to play "blind chess" like you, without tests and impatient simulations...

With "starting simple" I mean the framework implementation could be simple, feeding low resolution video and sound.

Boris Kazachenko said...

>Then this will be confirmed by the lack of progress

This was confirmed by the lack of progress over the last ~60 years.

Todor "Tosh" Arnaudov said...

OK, you win... I should learn to play "blind chess".

Boris Kazachenko said...

Go live in a cave.

Todor "Tosh" Arnaudov said...

:)) I'm going - next bus is tomorrow morning.