An event-driven mixed analog/digital spiking neural network circuit model for hippocampal spatiotemporal context learning and memory

The continuous-time/value extended spatiotemporal context learning and memory network (eSTCLMN) model shows promise for low-power edge AI hardware equipped with quick one-shot learning capability, due to its suitability for implementation in fully analog asynchronous event-driven spiking integrated circuits. However, designing analog integrated circuits requires pre-optimization of circuit parameters, which is challenging with software simulations of large differential equation system involving asynchronous spikes. To address this challenge, we discretize the eSTCLMN model in time and value, enabling its implementation in event-driven mixed analog/digital circuits. In this modified model, synaptic circuits operate asynchronously with their clocks, while neurons are built with fully continuous-time/value analog circuits. Thus, the proposed approximate eST-CLMN model is represented by a set of differential and difference equations. Moreover, we introduce an asynchronous event-driven mixed analog/digital proof-of-concept (POC) hardware system for the approximate eSTCLMN model. A small-size test circuit board for the POC system is fabricated using commercially available components. Experimental results on the test circuit board demonstrate the validity of the major components of the proposed circuitry for the approximate eSTCLMN model. Finally, we propose the hardware configuration of a medium-size eSTCLMN model circuit system.