1. A Theory of Cerebral Cortex (or, “How Your Brain Works”) Andrew Smith (CSE)

2. Outline Questions Preliminaries Feature Attractor Networks Antecedent Support Networks Attractive properties of the theory / Conclusions

3. Questions (to be answered!) What is cortical knowledge and how is it stored? How is it used to carry out thinking? How is it integrated with sensory input and motor output?

4. Preliminaries Thinking is a symbolic process. Thinking relies only on classical mechanics. (Unlike the Penrose/Hameroff model.) Thinking is not a mathematically grounded reasoning process, rather confabulation!

5. Feature Attractor Neuronal Networks An object (sensory, abstract, etc.) or action (movement process, thought process, etc.) is represented by a collection of feature attractor tokens, each expressing a single token (node) from its lexicon. cerebral cortex cortical region (one of about 120,000) thalamus paired thalamic region human cortical surface area  240,000 mm 2 Each region encompasses a cortical surface area of roughly 2 mm 2 and possesses a total of about 200,000 neurons. bidirectional connections Each Feature Attractor Network Implements one ‘Column’ of Tokens a feature attractor network

6. Feature Attractor Networks Each network has a lexicon of random (!) tokens, sparsely encoded; each token has 100’s of neurons on at a time, out of 50,000. This lexicon is fixed very early in life and never changes. The function of the network is to change the pattern of activation within a particular region so that it expresses the token in its lexicon “closest” to the original pattern of activation. (aka “vector quantizers”) The Feature Attractor Networks are extremely robust to noise/partial tokens. - A region can start out with 10% of a particular token and within one iteration, express the complete token. - A region can start out expressing many (100’s) of partial tokens and within one iteration, express just one token that was most complete. (more on this later…) Now we have ~120,000 powerful pattern recognizers, let’s wire them up…

7. Antecedent Support Networks (ASNs) The role of the ASN is to do the thinking. - If several active tokens have strong links to an inactive token, the ASN will activate that token (e.g. “smoke” + “heat” -> “fire”). - Learning occurs when the ASN increases the link weight between two tokens. Short term memory = Which tokens are currently active Long term memory = The link strengths between tokens

8. source region token i cerebral cortex transponder neurons target region token j Antecedent Support Neuronal Network Implementation – Randomness to the rescue! these are the synapses that are strengthened “Axons from neuron of token i send their collaterals randomly to millions of neurons in the local area. Of these, a few thousand transponder neurons just happen to receive sufficient input from i to become active. Of those, a few hundred just happen to send axons to neurons belonging to token j on the target region, activating (part of) token j.” The wiring of transponder neurons (pyramidal neurons) is also fixed at a very early age.

9. Input / Output Input: Sensory neurons connect to token neurons (layers III and IV), just like transponder neurons. Output: Motor neurons can receive their inputs from the token neurons, just like transponder neurons.

10. Attractive features (no pun intended…) The Hecht-Nielsen model shows: - how neurons can grow randomly and become organized. - that a large range of synaptic weights is not necessary. - how you can get a song stuck in your head. (You’re unable to reset regions of your cortex. One bar evokes the next…) - a model that can be viewed as implementing Paul Churchland’s “semantic maps” from last lecture of CogSci 200. (IMHO…) A simulation of this has solved the classic “cocktail-party problem.”

11. Conclusions “[All knowledge comes from creating associations between experiences.]” - Aristotle “Within 12 to 36 months, this theory will revolutionize Artificial Intelligence.” - Hecht-Nielsen (as of last week…)