## Wednesday, August 3, 2022

### The structured physical system hypothesis (SPSH), Polyviscous connectivity [The brain as a physical system]

Over in the discussion of Yann LeCun’s recent paper (A Path Towards Autonomous Machine Intelligence), Saty Chary has been arguing for something he calls the Structured Physical System Hypothesis (SPSH):

‘A structured physical system has the necessary and sufficient means for specific intelligent response’. By structured physical system, I mean, an analog design, e.g. a Rube Goldberg apparatus, or a Braitenberg (!) vehicle, etc. This is in contrast to this: PSSH - Physical Symbol System Hypothesis - 'A physical symbol system has the necessary and sufficient means for general intelligent action'.

I would add the slide rule as another example. From Wikipedia:

The slide rule is a mechanical analog computer, which is used primarily for multiplication and division, and for functions such as exponents, roots, logarithms, and trigonometry. It is not typically designed for addition or subtraction, which is usually performed using other methods. Maximum accuracy for standard linear slide rules is about three decimal significant digits, while scientific notation is used to keep track of the order of magnitude of results. [...]

At its simplest, each number to be multiplied is represented by a length on a pair of parallel rulers that can slide past each other. As the rulers each have a logarithmic scale, it is possible to align them to read the sum of the numbers' logarithms, and hence calculate the product of the two numbers.

My father used a slide rule for his entire career as an engineer. I learned to use one in my teens – everyone did back that – but never had any use for one. They’ve been replaced by cheap electronic calculators and PCs.

But that’s a digression. It’s the more general Structured Physical System Hypothesis that interests me, as Saty has been argued that the brain is such a system I agree (see this recent post, Once more around the merry-go-round: Is the brain a computer?). Here’s my reply to Saty:

Hi, Saty. I like your hypothesis – Structured Physical System Hypothesis (SPSH) – a lot. I think that a lot about the brain is consistent with it. For example, we know that mappings from one area to another as we move from the sense organs (or muscles), to the subcortex, and into the cortex tend to preserve topological relations between neurons. That’s of obvious value in the visual and motor systems. But there are subtleties. In the cortical visual system we have a so-called What-system and a so-called Where-system beyond the primary cortex. The What-system tracks location in space while the Where-system identifies objects. I assume that the Where-system has links to the hippocampus and I’d expect it can deal with both ego-centric and geocentric coordinates (this must be in the literature). But I’d think the What-system deals in object-centered coordinates. And so forth and so on.

In thinking about Freeman’s results (reported in this post), and others, I’ve coined a phrase: polyviscous connectivity. Thus I say that the cortical network as a whole exhibits polyviscous connectivity. What do I mean? Some connections are highly resistant to change, and thus have high viscosity. Others change quite readily, and have low viscosity. There is a literature on long-term (LTP) and short-term potentiation (STP) of neural connectivity that is certainly relevant here, but I’ve not looked at it in quite a while.

Consider Freeman’s results. He’s measuring neural activity with an 8 by 8 array of electrodes mounted on the cortical surface. They’re going to detect activity of neurons at varying levels of viscosity. Let’s a assume that the patterns of connectivity encoding odorants that rat already recognizes have a relatively high viscosity. Let’s further assume that the neurons most susceptible to learning new odorants have a relatively low viscosity.

Once they’ve formed a stable response to the new odorant that will result in a new pattern of neural activity for the ensemble. But it is also going to change the patterns exhibited by already learned odorants even though the high-viscosity connections haven’t changed. The high viscosity connections maintain the overall integrity of the ensemble. In time, if the new odorant continues to be encountered, the connections registering it will increase in viscosity. So, polyviscous connectivity allows a structured connectionist physical system to maintain its overall integrity while adding new items to its repertoire.

More later.

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Note: Looking around on my hard-drive I found an article which is about what I have called polyviscosity, though it doesn’t use that term:

Poonam Mishra and Rishikesh Narayanan, Stable continual learning through structured multiscale plasticity manifolds, Current Opinion in Neurobiology 2021, 70:51–63, https://doi.org/10.1016/j.conb.2021.07.009

Abstract: Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type–specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.

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Note: I’d previously been using the terms “hyperciscosity” or “hyperviscous”, and you’ll find them in my posts and notes on this topic going back to 2013 (when I wrote about From Associative Nets to the Fluid Mind). But I have reluctantly decided to coin a new term since “hyperviscosity” is already being used.

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Addendum, 8.13.22: On the Structured Physical System Hypothesis, see this post where I feature remarks by Rodney Brooks, Has the computer metaphor for the mind run out of steam? As the title suggests, Brooks is wondering whether or not it makes sense to think about nervous systems in terms of computation. Thus he wonders:

Is information processing the right metaphor there? Or are control theory and resonance and synchronization the right metaphor? We need different metaphors at different times, rather than just computation. Physical intuition that we probably have as we think about computation has served physicists well, until you get to the quantum world. When you get to the quantum world, that physical intuition about stuff and place gets in the way.

#### 2 comments:

1. In natural human languages with a great many (circa 1000 or more) ideophones, the phonosemantic mappings (form/meaning links between phonemes (or even distinctive features) often are concerned with actual physicomechanical visco-elasticity relations. Viscous materials share energy and often mass- the components will freely mix to some degree, leading to a sort of self-healing when two viscous bodies are brought into contact (if the measured viscosity is low enough and the materials miscible). Physicomechanically elastic materials, on the other had, do NOT share energy or mass, and when bonds are broken they STAY broken barring some sort of other transformation that causes local or global melting, etc.). I wonder whether there are any analogues of elasticity in the neural system- architecturally or behaviorally. By the way, in most of the languages I've examined, bisyllabic ideophones usually have one syllable dedicated to the material properties of the entity being described, and the other to its larger external spatiotemporal context. This is the way it works in Japanese, Korean, Nanai (Tungusic), and Zulu. Parallel to the 'what' and 'where' paths in cortical regions??

1. Interesting set of observations.