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

Storage capacity of phase-coded patterns in sparse neural networks