Stochastic Ising model with plastic interactions

TL;DR

This paper introduces a stochastic Ising model with dynamic couplings to simulate synaptic plasticity in neural networks, capturing memory formation mechanisms through coupled stochastic dynamics.

Contribution

It presents a novel Ising-based model where both spins and couplings evolve stochastically, integrating synaptic plasticity into statistical physics frameworks.

Findings

Couplings exhibit stochastic dynamics influenced by neural activity.

Model captures key features of synaptic strengthening and memory formation.

Continuous-time Markov process describes the evolution of neural connections.

Abstract

We propose a new model based on the Ising model with the aim to study synaptic plasticity phenomena in neural networks. It is today well established in biology that the synapses or connections between certain types of neurons are strengthened when the neurons are co-active, a form of the so called synaptic plasticity. Such mechanism is believed to mediate the formation and maintenance of memories. The proposed model describes some features from that phenomenon. Together with the spin-flip dynamics, in our model the coupling constants are also subject to stochastic dynamics, so that they interact with each other. The evolution of the system is described by a continuous-time Markov jump process. Keyword Markov chain, Stochastic Ising model, synaptic plasticity, neural networks, transience