Neuromorphic Computing in Autoassociative Memory with a Regular Spiking Neuron Model

Abstract

Digital Spiking Silicon Neuron (DSSN) model is a qualitative neuron model specifically designed for efficient digital circuit implementation which exhibits high biological plausibility. In this study we analyzed the behavior of an autoassociative memory composed of 3-variable DSSN model which has a slow negative feedback variable that models the effect of slow ionic currents responsible for Spike Frequency Adaptation (SFA). We observed the network dynamics by altering the strength of SFA which is known to be dependent on Acetylcholine volume, together with the magnitude of neuronal interaction. By altering these parameters, we obtained various pattern retrieval dynamics, such as chaotic transitions within stored patterns or stable and high retrieval performance. In the end, we discuss potential applications of the obtained results for neuromorphic computing.