Learning with Synaptic Nonlinearities in a Coupled Oscillator Model of Olfactory Cortex

A simple network model of olfactory cortex, which assumes only minimal coupling justified by known anatomy, can be analytically proven to function as an associative memory for oscillatory patterns. The network has explicit excitatory neural populations with local inhibitory interneu- ron feedback that forms a set of nonlinear oscillators coupled only by long range excitatory connections. Using a local Hebb-like learning rule for primary and higher order synapses at the ends of the long range connections, the system can learn to store the kinds of oscillation amplitude and phase patterns observed in olfactory and visual cortex. The network can be truely self-organizing because a synapse can modify itself according to it’s own pre and postsynaptic activity during stimulation by an input pattern to be learned. The neurons of the neuron pools modeled here can be viewed as operating in the linear region of the usual sigmoidal axonal non- linearity, and multiple memories are stored instead by the learned synaptic nonlinearities