By Afshin Montakhab (California Polytechnic State University, San Luis Obispo, California). November 8th, 2024.
In this work, we study a discrete probabilistic neuron-based model on a random network governed by specific dynamic rules to investigate potential phase transitions, including absorbing, synchronization, and chaotic transitions. The primary objective is to determine how these transitions contribute to the emergence of criticality. We first identify the associated transition points and investigate the model’s collective dynamics at such transitions. Next, we impose synaptic plasticity by introducing dynamical rules among the connections between the neurons. The results demonstrate that the network tends to self-organize toward the synchronization phase transition, a critical point where collective behavior emerges. At this boundary, the system exhibits hallmark features of criticality, such as scale-invariant avalanche distributions with experimentally relevant exponents. Our results show that simple plasticity (STDP) can self-organize the model near a synchronization transition with experimentally relevant indicators of criticality.
Contact: Afshin Montakhab, motomonty@gmail.com
Additional authors: Ehsan Ziarati (University of Arkansas, Fayetteville, AR), Afshin Montakhab (California Polytechnic State University, San Luis Obispo, CA)