Indicizzato scopus: eid=2-s2.0-84867909032 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 SpikeTiming 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
Attractor networks and memory replay of phase coded spike patterns
GIACCO, FERDINANDO;SCARPETTA, Silvia
2011-01-01
Abstract
Indicizzato scopus: eid=2-s2.0-84867909032 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 SpikeTiming 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 networkI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
