Storage capacity of phase-coded patterns in sparse neural networks

Details Storage capacity of phase-coded patterns in sparse neural networks Storage capacity of phase-coded patterns in sparse neural networks

2011-06-10

arxiv.org/abs/1106.2032v1

Biology

Quantitative Biology

Neurons and Cognition

Accepted for publication in Europhysics Letters

Scientific paper

We study the storage of multiple phase-coded patterns as stable dynamical attractors in recurrent neural networks with sparse connectivity. To determine the synaptic strength of existent connections and store the phase-coded patterns, we introduce a learning rule inspired to the spike-timing dependent plasticity (STDP). We find that, after learning, the spontaneous dynamics of the network replay one of the stored dynamical patterns, depending on the network initialization. We study the network capacity as a function of topology, and find that a small- world-like topology may be optimal, as a compromise between the high wiring cost of long range connections and the capacity increase.

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