Spiking Optical Patterns and Synchronization

TL;DR

This paper investigates the spike timing and synchronization in chaotic semiconductor lasers, revealing patterns similar to neural networks and suggesting their use as simplified models for neural dynamics.

Contribution

It provides a detailed analysis of spike statistics and synchronization in chaotic lasers, highlighting their potential as physical models for neural networks.

Findings

Spike repulsion creates a refractory period largest at laser threshold.

Spike intervals follow a Poisson distribution beyond the refractory period.

Zero-lag synchronization does not alter spike statistics.

Abstract

We analyze the time resolved spike statistics of a solitary and two mutually interacting chaotic semiconductor lasers whose chaos is characterized by apparently random, short intensity spikes. Repulsion between two successive spikes is observed, resulting in a refractory period which is largest at laser threshold. For time intervals between spikes greater than the refractory period, the distribution of the intervals follows a Poisson distribution. The spiking pattern is highly periodic over time windows corresponding to the optical length of the external cavity, with a slow change of the spiking pattern as time increases. When zero-lag synchronization between the two lasers is established, the statistics of the nearly perfectly matched spikes are not altered. The similarity of these features to those found in complex interacting neural networks, suggests the use of laser systems as simpler physical models for neural networks.