Locking-time and Information Capacity in CML with Statistical Periodicity

TL;DR

This paper investigates the statistical periodicity in coupled map lattices, showing that weak coupling leads to low locking-time and high information capacity, similar to neural network behavior.

Contribution

It introduces a novel analysis of statistical periodicity in CMLs using asymptotic binary patterns, linking pattern multiplicity and entropy rate to information capacity and locking-time.

Findings

Weak coupling results in low locking-time.

Weak coupling yields high information capacity.

System behavior resembles neural networks under certain conditions.

Abstract

In this work we address the statistical periodicity phenomenon on a coupled map lattice. The study was done based on the asymptotic binary patterns. The pattern multiplicity gives us the lattice information capacity, while the entropy rate allows us to calculate the locking-time. Our results suggest that the lattice has low locking-time and high capacity information when the coupling is weak. This is the condition for the system to reproduce a kind of behavior observed in neural networks.