Associative Memory in a Simple Model of Oscillating Cortex

Abstract

A generic model of oscillating cortex, which assumes "minimal" coupling justified by known anatomy, is shown to function as an as(cid:173) sociative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long range excitatofy connections. Using a local Hebb-like learning rule for primary and higher order synapses at the ends of the long range connections, the system learns to store the kinds of oscil(cid:173) lation amplitude patterns observed in olfactory and visual cortex. This rule is derived from a more general "projection algorithm" for recurrent analog networks, that analytically guarantees content addressable memory storage of continuous periodic sequences - capacity: N /2 Fourier components for an N node network - "spurious" attractors.