Hebbian imprinting and retrieval in oscillatory neural networks
Neural Computation
Phase precession through synaptic facilitation
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
Journal of Computational Neuroscience
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We study the storage of phase-coded patterns as stable dynamical attractors in recurrent spin neural networks with small-world topology. The synaptic strength of existent connections is determined by a learning rule based on spike-time-dependent plasticity (STDP), with an asymmetric time window depending on the relative timing between pre-and post-synaptic activity. We store multiple patterns and study the network capacity in sparse networks with different topologies. We study networks where each neuron is connected only to a small number z « N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology maybe optimal, as a compromise between the cost of long range connections and the capacity increase.