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

摘要

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 the 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 premise. With mean field analysis, we derive the global criticality from local dynamics and achieve a power-law distribution. Critical behavior of EDM exhibits the convergent dynamics of Boltzmann distribution, and we conclude that the EDM model inherits the property of self-organized criticality, i.e., the system will eventually evolve toward criticality.