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Experience-Induced Neural Circuits That Achieve High Capacity

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Executive Summary

Over a lifetime cortex performs a vast number of different cognitive actions, mostly dependent on past experience. Previously it has not been known how such capabilities can be reconciled, even in principle, with the known resource constraints on cortex, such as low connectivity and low average synaptic strength. Here the paper describes neural circuits and associated algorithms that respect the brain's most basic resource constraints and support the execution of high numbers of cognitive actions when presented with natural inputs. The circuits simultaneously support a suite of four basic kinds of task that each require some circuit modification: hierarchical memory formation, pairwise association, supervised memorization, and inductive learning of threshold functions.

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