A Spiking Neural Dynamical Drift-Diffusion Model on Collective Decision Making with Self-Organized Criticality

TL;DR

This paper introduces a novel spiking neural network model called EDM that models collective decision making, demonstrating self-organized criticality and power law behavior through analytical and experimental analysis.

Contribution

The paper presents the Exponential Decision Making (EDM) model using spiking neural networks to capture collective decision dynamics and demonstrate self-organized criticality.

Findings

Gating variable follows Boltzmann distribution

System reaches meta-stable critical states

Exhibits power law distribution and self-organized criticality

Abstract

This article proposes a novel collective decision making scheme to solve the multi-agent drift-diffusion-model problem with the help of spiking neural networks. The exponential integrate-and-fire model is used here to capture the individual dynamics of each agent in the system, and we name this new model as Exponential Decision Making (EDM) model. We demonstrate analytically and experimentally that the gating variable for instantaneous activation follows Boltzmann probability distribution, and the collective system reaches meta-stable critical states under the Markov chain premises. With mean field analysis, we derive the global criticality from local dynamics and achieve a power law distribution. Critical behavior of EDM exhibits the convergence dynamics of Boltzmann distribution, and we conclude that the EDM model inherits the property of self-organized criticality, that the system will eventually evolve toward criticality.