ApeWorm Brain Overview

(part of the series Aping As the Basis of Intelligence)

Figure 2 gives an overview of the ApeWorm "central nervous system" or control system. 

Two eyes or retinas gather information about the exemplar that is analyzed to form an exemplar inner model. The ApeWorm also analyzes it internal sensors to construct a self inner model, or self image. If they match, the Ape control function registers success and indicates to the muscle control functions to stop movement. It also may reinforce the "synapses" or weights that led to the success, including any temporal sequence involved. If there is No Match, muscle control allows movement to continue.

It should be noted that the compartmentalization of functions in Figure 2 need not necessarily correspond to how the system is constructed. A neural model might make it possible, or even necessary, to tightly integrate two or more, or even all, of the functions that seem separate in this analysis.

Bottom Up Machine Understanding

The first paragraph of "Towards a Mathematical Theory of Cortical Micro-circuits" [Dileep George & Jeff Hawkins, PLoS Computational Biology, October 2009, Volume 5, Issue 10] states:

Understanding the computational and informtion processing roles of cortical circuitry is one of the oustanding problems in neuroscience. ... the data are not sufficient to derive a computational theory in a purely bottom-up fashion

My own cortex, probably not wanting to give itself a headache by proceeding too rapidly into what looks like a dense and difficult paper, immediately drifted off into thoughts on deriving a computational theory in a purely bottom-up fashion.

The closer we get to the physical bottom, the easier it seems to be to understand that the project might work. Suppose we could model an entire human brain at the molecular level. We imagine a scanner that can tell us, for a living person who we admit is intelligent and conscious, where each molecule is to a sufficient degree of exactitude. We also would have a computational system for the rules of molecular physics, and appropriate inputs and outputs.

Unless you believe that the human mind is not material (a dualist or idealist philosophic view), such a molecular-detail model should run like a human brain. At first it should think (and talk and act, to the extent the outputs allow) exactly like the person whose brain was scanned.

However, that does not mean scientists would understand how the brain works, or how a computational machine could exhibit understanding. Reproducing a phenomena and understanding a phenomena are not the same thing. The advantage of such a molecular computional brain model would be that we could run experiments on it in a way that could not be done on human beings or even on other mammals. We could start inputting and tracing data flows. We could interrupt and view the model in detail at any time. We could change parameters and isolate subsystems. Perhaps, further in the future, such a model could even be constructed without having to scan a human brain for the initial state.

At present, for a bottoms-up approach that might actually be workable in less than a decade, we would probably want to do a neuron-by-neuron model (probably with all the non-neural supporting cells in the brain as well). However, a lot of new issues arise even at this seemingly low level, even if we presume we have some way to scan into the model all of the often-complicated axon to dendrite paths and synapses. If learning is indeed based on synapse strength (Hebbian hypothesis), we would need both a good model for synapse dynamics and a detailed original state of synapses. This would require modeling the synapses themselves at the molecular level, or perhaps one-level up at the some molecular aggregate level. In effect it would not be possible to model an adult brain that has exhibited intelligence and understanding. We would need to start with a baby brain and hope that it would go through a pattern of neural information development similar to that of a human child.

A complete neural-level model would be much easier to test intelligence hypothesese on than a molecular-level model. It would not in itelf indicate that we understand how humans understand the world. By running the model in two parallel instances (with identical starting states), but with selected malfunctions, we could probably isolate most of the sub-systems required for intelligence. This should help us build a comprehensible picture of how intelligence operates once it is established and of how it can be constructed by neural circuits from whatever the raw ingredients of intelligence turn out to be.

Despite our lack of such complete bottom-up models, I don't think it is too early to try to reconstruct how the brain works, or how to make machines intelligent. The paper outlines the HTM approach to this subject. HTM was based on a great deal of prior work in neuroscience and in modeling neural aggregates with computers. Often in science success has come from the combination of bottom-up and top-down approaches. Biological species, and fossil species, were long catalogued and studied before Darwin's theory of evolution revealed the connections between species. Darwin did not invent the concept of evolution of species, or of inherited traits, which many scientists already believed were shown by the fossil record and living organisms. He added the concept of natural selection, and suddenly evolution made sense. The whole picture popped into view in a way that any intelligent human being could see.

Synaptic Transmission Puzzle

"Over the past twenty years, a wide variety of modes of synaptic transmission have been discovered, in addition to simple, chemically mediated increases in permeability leading to excitation and inhibiation. Such modes of transmission include electrical exictation and inhibition, combined chemical-electrical synapses, chemical synapted changes produced by reductions in memberane permeability, and prolonged synaptic potentials mediated by chemical reactions in the postsynaptic cell."
-- page 208, From Neuron to Brain, Second Edition, by Stephen W. Kuffler, John G. Nicholls, and A. Robert Martin; 1984, Sinauer Associates, Inc., Suderland, Massachusetts

In Jeff Hawkins' On Intelligence, he presents a predictive-memory model for human intelligence which he believes can serve as a basis for intelligent machines [which I call machine understanding to emphasize that the machines would not merely be mimicking surface features of human intelligence, as is the case with, for example, expert systems]. I agree with him that while neuroscience and computing science have made great progress in understanding many details of the human brain, and in writing software, we need to have an overview that allows us to make progress on the centrals issues that interest us. Thus, in Hawkins' model, he does not worry about the exact nature of neural synapses.

But at the very least, we should be aware of how complicated human synapses are. This should allow us a greater freedom of thought when modeling the mechanics of machine understanding than if we simply assumed synapses all work alike. I suspect the Hebbian learning model for neurons would benefit from considering that the real world may complex, and in that complexity there may be keys to progress that we leave out with overly simple models.

What struck me most about the above paragraph about synapses, however, is the role played by evolution. Charles Darwin wrote about the process of species creation, but we have long grown used to the idea that evolution takes place on a molecular level. Each nerve cell, presumably, contains the full set of genes of the organism, but many different types of synapses are manifested. There must be controlling, blueprint genes that tell the cells which typese of synapses they are to form as they develop. In turn, we can expect that many different blueprints have been tried over the last four million years or so. The most successful gave their human organisms survival advantages.

It would be interesting to know how much synaptic variation exists in the current human population. Is this variation responsible, or partly responsible, for variations in basic intelligence capabilities of human beings?

This brings us back to the hard-wiring versus plastic debate. Human beings are very adaptable, as is shown by their ability to learn any of thousands of human languages as a child. We tend to think that we are very plastic and programmable creatures. But nerve transmission speeds and synaptic types are hard wired, as is the basic design of the brain. One might say we have the hard-wired capability to be flexible.

And that is what we aim to build into the new machines: hard wiring (a stable system with a design we understand and can reproduce) that is capable of showing the flexibility required to exhibit intelligence and understanding.