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Experience-induced neural circuits that achieve high capacity.

Vitaly Feldman¹, Leslie G Valiant

¹IBM Almaden Research Center, San Jose, CA 95120, USA. vitaly.edu@gmail.com

Neural Computation

|July 29, 2009

Summary

This study introduces novel neural circuits and algorithms that enable the cortex to perform numerous cognitive actions within biological constraints. These biologically plausible models demonstrate efficient learning and memory, reconciling brain resource limitations with complex cognitive functions.

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

The cortex performs many cognitive actions dependent on experience.
Reconciling these capabilities with resource constraints (low connectivity, low synaptic strength) has been a challenge.
Existing theories lack biologically plausible, systems-level demonstrations.

Purpose of the Study:

To describe neural circuits and algorithms that respect brain resource constraints.
To support a high number of cognitive actions with natural inputs.
To demonstrate a biologically plausible systems-level theory of learning and memory.

Main Methods:

Development of novel neural circuits and algorithms.
Simulation of thousands of cognitive actions using computer experiments.
Testing the efficacy of created circuits for various cognitive tasks.

Main Results:

Circuits simultaneously support hierarchical memory, pairwise association, supervised memorization, and inductive learning.
Computer simulations validated the capacity and efficacy of the circuits.
The approach respects fundamental resource constraints of the cortex.

Conclusions:

The described circuits and algorithms offer a biologically plausible solution for cognitive action execution within brain constraints.
This work presents the only known biologically plausible systems-level theory of cortical learning and memory with experimental demonstration.
A viable theory of brain information processing requires such empirical validation.