Nonlinear Characteristics of Neural Signals

TL;DR

This paper investigates the nonlinear properties of neural signals, revealing their scale invariance and constant entropy in cases of self-similarity and self-affinity, using theoretical models.

Contribution

It introduces generalized metrical and topological measures for neural signals and demonstrates their scale-invariant and entropy characteristics.

Findings

Neural signals exhibit scale invariance in action potential time dependence.

Information and entropy remain constant for self-similar and self-affine signals.

Theoretical models effectively describe nonlinear neural signal properties.

Abstract

The study is devoted to definition of generalized metrical and topological (informational entropy) characteristics of neural signals via their well-known theoretical models. We have shown that time dependence of action potential of neurons is scale invariant. Information and entropy of neural signals have constant values in case of self-similarity and self-affinity.