2 research outputs found
The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons
We study associative memory neural networks based on the Hodgkin-Huxley type
of spiking neurons. We introduce the spike-timing-dependent learning rule, in
which the time window with the negative part as well as the positive part is
used to describe the biologically plausible synaptic plasticity. The learning
rule is applied to encode a number of periodical spatiotemporal patterns, which
are successfully reproduced in the periodical firing pattern of spiking neurons
in the process of memory retrieval. The global inhibition is incorporated into
the model so as to induce the gamma oscillation. The occurrence of gamma
oscillation turns out to give appropriate spike timings for memory retrieval of
discrete type of spatiotemporal pattern. The theoretical analysis to elucidate
the stationary properties of perfect retrieval state is conducted in the limit
of an infinite number of neurons and shows the good agreement with the result
of numerical simulations. The result of this analysis indicates that the
presence of the negative and positive parts in the form of the time window
contributes to reduce the size of crosstalk term, implying that the time window
with the negative and positive parts is suitable to encode a number of
spatiotemporal patterns. We draw some phase diagrams, in which we find various
types of phase transitions with change of the intensity of global inhibition.Comment: Accepted for publication in Physical Review