Extraction of Phase Information Buried in Fluctuation of a Pulse-type Hardware Neuron Model Using STDP

Since neural networks have superior information processing functions, many investigators attempt to model biological neurons and their networks. Furthermore, a number of studies of neural networks have recently been made with the purpose of applying engineering to the brain. In this study, we investigate the effect of STDP on the ability to extract phase information buried in fluctuation. We focus on spike timing dependent synaptic plasticity (STDP), and we construct neural networks from a pulse-type hardware neuron model using STDP. We show that phase information buried in fluctuation is revealed by the effect of STDP, making it possible to decode the synaptic weight. Moreover, we show that it is possible to extract the phase difference buried in fluctuation representing the reinforcement part of the synaptic weight, using neural networks with STDP.