Temporal Information Processing and Stability Analysis of the MHSN Neuron Model in DDF

Abstract

-Implementation of a neuron like information processing structure at hardware level is a burning research problem. In this article, we analyze the modified hybrid spiking neuron model (the MHSN model) in distributed delay framework (DDF) for hardware level implementation point of view. We investigate its temporal information processing capability in term of inter-spike-interval (ISI) distribution. We also perform the stability analysis of the MHSN model, in which, we compute nullclines, steady state solution, eigenvalues corresponding the MHSN model. During phase plane analysis, we notice that the MHSN model generates limit cycle oscillations which is an important phenomenon in many biological processes. Qualitative behavior of these limit cycle does not changes due to the variation in applied input stimulus, however, delay effect the spiking activity and duration of cycle get altered.

Editor's Note

“…Choudhary and Singh [7] perform the analysis of stability and temporal information processing capability of a hybrid spiking neuron model in distributed delay framework. The approach focuses on the hardware level implementation of artificial neurons.…”

Information Processing in Neuron with Exponential Distributed Delay

“…Neural networks are artificial human-brain like computing structure which incorporates Lapicque (1907) has proposed the integrate-and-fire neuron model (IF model), first neuron model, equivalent to an electrical RC-circuit with an additional threshold constraint, i.e. neuron potential resets a lower value as soon as it reaches to a certain value (threshold) [1], [7], [9]. Under the influence of noisy input stimulus and noisy environment, neuron reflects the highest level of variation in spiking pattern [25].…”