Encoding Temporal Relations using Complex-Valued Synapses

Abstract

In this work, we show that by representing the neural activity and synaptic activity in the complex domain, it is possible to encode temporal relations among the neural oscillations. The complex representation is also able to bring out learning property like Spike-Timing Dependent Plasticity (STDP) observed in a network of neurons.

Publication
Annual conference of the Association for Cognitive Science (ACCS)
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