Information reduction in a reverberatory neuronal network through convergence to complex oscillatory firing patterns

TL;DR

This study uses computer simulations of a neural network to explore how diverse stimuli lead to convergence into complex oscillatory firing patterns, potentially modeling perception and memory processes.

Contribution

It demonstrates how a reverberating neural network can self-organize into multiple periodic states from various stimuli, highlighting the role of neuronal firing in this process.

Findings

83% of stimuli led to convergence into periodic states

102 distinct periodic end-states identified

Neuronal firing is essential for trajectory merging

Abstract

We study dynamics of a reverberating neural net by means of computer simulation. The net, which is composed of 9 leaky integrate-and-fire (LIF) neurons arranged in a square lattice, is fully connected with interneuronal communication delay proportional to the corresponding distance. The network is initially stimulated with different stimuli and then goes freely. For each stimulus, in the course of free evolution, activity either dies out completely or the network converges to a periodic trajectory, which may be different for different stimuli. The latter is observed for a set of 285290 initial stimuli which constitutes 83% of all stimuli applied. By applying each stimulus from the set, we found 102 different periodic end-states. By analyzing the trajectories, we conclude that neuronal firing is the necessary prerequisite for merging different trajectories into a single one, which eventually transforms into a periodic regime. Observed phenomena of self-organization in the time domain are discussed as a possible model for processes taking place during perception. The repetitive firing in the periodic regimes could underpin memory formation.