Analysis of Learning Process of Synaptic Weights in Spatio-temporal Learning Networks for Hardware Implementation

A spatio-temporal learning (STL) rule was developed based on the results of physiological experiments in the hippocampus. In contrast to the general Hebbian learning rule, the STL rule updates synaptic weight values through the internal states of neurons, depending on the coincidence of input patterns. This allows the STL rule to learn slight differences in the contextual structure of the spatio-temporal patterns. In the original STL network (STLnet) model, the synaptic weight values take both positive and negative. In this study, considering physiological knowledge and hardware implementation, we propose a modified STLnet model in which synapses are separated into excitatory and inhibitory that take on only positive and negative values, respectively. We then analyze the learning performance of the STLnet models. We confirm that the modified STLnet model has comparable learning performance to the original STLnet model. For the hardware implementation of the modified STLnet, we determine the optimal ratio of excitatory to inhibitory synapses. This study establishes important design guidelines for synaptic weights of the STLnet model.