Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model

TL;DR

This paper introduces a biologically plausible self-optimizing spiking neural network model that enhances neural coordination through occasional firing pattern interruptions, bridging rate-based and temporal coding systems.

Contribution

It demonstrates that self-optimization via firing pattern interruptions can be implemented in spiking neural networks, extending previous rate-based models to more realistic neural systems.

Findings

Self-optimization improves attractor basin sizes.

The model links rate-based and temporal coding.

Efficacy is independent of conventional assumptions.

Abstract

The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfy constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains