Attractor networks and memory replay of phase coded spike patterns

TL;DR

This paper investigates how a recurrent spiking neural network can store and recall phase-coded spike patterns, demonstrating selective oscillatory activity matching stored patterns through STDP learning and recall modes.

Contribution

It introduces a model combining STDP learning with recall dynamics in spiking neurons, showing capacity for storing and retrieving phase-coded patterns.

Findings

Network can store and recall phase-coded patterns.

Recall dynamics produce oscillations matching stored patterns.

Memory retrieval depends on initial network state.

Abstract

We analyse the storage and retrieval capacity in a recurrent neural network of spiking integrate and fire neurons. In the model we distinguish between a learning mode, during which the synaptic connections change according to a Spike-Timing Dependent Plasticity (STDP) rule, and a recall mode, in which connections strengths are no more plastic. Our findings show the ability of the network to store and recall periodic phase coded patterns a small number of neurons has been stimulated. The self sustained dynamics selectively gives an oscillating spiking activity that matches one of the stored patterns, depending on the initialization of the network.