An Extended Spatiotemporal Contextual Learning and Memory Network Model for Hardware Implementation

Abstract

A spatiotemporal contextual learning and memory network (STCLMN) model was proposed based on the hippocampal CA1 network using the difference equations suitable for digital computer simulations. However, the inherent capability of handling and processing of continuous-time asynchronous signals (spikes) cannot be fully employed in the STCLMN model. Furthermore, digital circuit implementation of the STCLMN is not suitable for real-time edge applications, wherein small size, low power consumption, and real-time learning capability are necessary. Therefore, this study extends the original STCLMN model using a set of differential equations to overcome the above problems encountered by STCLMN for asynchronous circuit implementation. The minimum accuracy required for a compact and low-power hardware implementation of the proposed model is then investigated. The validity and feasibility of the proposed model are confirmed through numerical simulations and experiments with a small mixed analog/digital circuit based on the investigated accuracy. In this study, LIF neurons are used because of their ease of circuit implementation. The proposed eSTCLMN model would enable an efficient circuit implementation of an asynchronous spiking neural network with spatiotemporal contextual one-shot learning and memory.