An associative memory of Hodgkin-Huxley neuron networks with Willshaw-type synaptic couplings

Abstract

An associative memory has been discussed of neural networks consisting of spiking N (=100) Hodgkin-Huxley (HH) neurons with time-delayed couplings, which memorize P patterns in their synaptic weights. In addition to excitatory synapses whose strengths are modified after the Willshaw-type learning rule with the 0/1 code for quiescent/active states, the network includes uniform inhibitory synapses which are introduced to reduce cross-talk noises. Our simulations of the HH neuron network for the noise-free state have shown to yield a fairly good performance with the storage capacity of ${\alpha }_c=P_{\max }/N\sim 0.4-2.4$ for the low neuron activity of $f\sim 0.04-0.10$. This storage capacity of our temporal-code network is comparable to that of the rate-code model with the Willshaw-type synapses. Our HH neuron network is realized not to be vulnerable to the distribution of time delays in couplings. The variability of interspace interval (ISI) of output spike trains in the process of retrieving stored patterns is also discussed.

15 pages, 3 figures, changed Title

References in corpus (4)

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Cited by in corpus (3)

• The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons
• Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons
• Reinforcement learning of recurrent neural network for temporal coding