A simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis

TL;DR

This paper introduces a simple stochastic Hebbian learning model that maintains neural activity and connectivity homeostasis, predicts epileptogenic states post-seizure, and suggests activity-boosting interventions as protective.

Contribution

It presents a novel model integrating firing rate and critical homeostasis, and explores its implications for epilepsy and neural stability.

Findings

Supercritical states are associated with epileptogenesis.

Post-seizure and deafferentation states tend to be supercritical.

Boosting spontaneous activity may prevent epileptogenesis.

Abstract

A spontaneously active neural system that is capable of continual learning should also be capable of homeostasis of both firing rate and connectivity. Experimental evidence suggests that both types of homeostasis exist, and that connectivity is maintained at a state that is optimal for information transmission and storage. This state is referred to as the critical state. We present a simple stochastic computational Hebbian learning model that incorporates both firing rate and critical homeostasis, and we explore its stability and connectivity properties. We also examine the behavior of our model with a simulated seizure and with simulated acute deafferentation. We argue that a neural system that is more highly connected than the critical state (i.e., one that is "supercritical") is epileptogenic. Based on our simulations, we predict that the post-seizural and post-deafferentation states should be supercritical and epileptogenic. Furthermore, interventions that boost spontaneous activity should be protective against epileptogenesis.