Experience-Induced Neural Circuits That Achieve High Capacity

Abstract

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 we describe 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. Our 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. The capacity of our circuits is established via experiments in which sequences of several thousands of such actions are simulated by computer and the circuits created tested for subsequent efficacy. Our underlying theory is apparently the only biologically plausible systems-level theory of learning and memory in cortex for which such a demonstration has been performed, and we argue that no general theory of information processing in the brain can be considered viable without such a demonstration.