Bifurcation Analysis Framework of Spiking Neuron Models

TL;DR

This paper introduces a bifurcation analysis framework based on nonlinear dynamical systems theory to evaluate and optimize the nonlinear behavior of various spiking neuron models for neuromorphic computing.

Contribution

It provides a standardized analysis method for different neuron models and physical implementations, aiding in design and optimization of artificial neurons.

Findings

Defines Hopf bifurcation conditions for neuron firing states

Predicts near-onset firing rates across models

Offers design guidelines for neuron firing properties

Abstract

Neuromorphic computing targets energy-efficient event-driven information processing by placing artificial spiking-neurons at its core. Artificial neuron devices and circuits have multiple operating modes and produce region-dependent nonlinear dynamics that are not captured by system analysis methods for linear systems, such as transfer functions, Fourier/Laplace transform, Bode diagram, etc. Thus, new tools are needed to evaluate the nonlinear behavior of neurons and to guide the design and optimization of artificial neuron implementations. Here we present a generalized bifurcation analysis framework based on nonlinear dynamical systems theory. A CMOS axon-hillock neuron, memristor neuron, and the FitzHugh-Nagumo biological neuron model are selected for demonstration. We evaluate Hopf bifurcation conditions to define the rest and firing domains in parameter space of the system, and predict the near-onset firing rate. The results are further compared with numerical simulations. The framework standardizes the analysis for various neuron models and physical realizations. It yields practical design and optimization guidelines for artificial neurons for neuromorphic computing systems, including parameter combinations to make a neuron fire/rest and to control the corresponding firing properties, e.g., firing rate and amplitude.