Learning by Glueing

I’ve just read the wikipedia entry on neural darwinism.

Reentry

The last part of the theory attempts to explain how we experience spatiotemporal consistency in our interaction with environmental stimuli. Edelman proposes a model of reentrant signaling whereby a disjunctive, multimodal sampling of the same stimulus event correlated in time leads to self-organizing intelligence. Put another way, multiple neuronal groups can be used to sample a given stimulus set in parallel and communicate between these disjunctive groups with incurred latency.

The index i in ( h_i, X_i ) may be time itself. In this case, the calculation of majority and veto in the Pattern Engine can be done by a simple integrator. When the integrated majority vote is high enough, the result is fed back into storage. A veto or high feedback activity will quench the feedback path.

Update: We may start with some spaces X_i and increase their number by:

1. projecting into still smaller spaces
2. generating copies with different time delays
3. taking products to get spaces of the right size, neither too small nor too large.

The effects of glueing along a temporal index will look like prediction.

The theory of neural darwinism has been criticised on the ground that there is selection but no reproduction in this system ( Wikipedia). This is food for thought.

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