Route to chaos in a branching model of neural network dynamics

TL;DR

This paper investigates the chaotic transition in a simplified neural network model, the cortical branching model, revealing its unique properties and differences from classical maps like the Hénon map, advancing understanding of neural chaos.

Contribution

It characterizes the chaotic transition in the mean-field approximation of the CBM, highlighting its differences from the Hénon map and introducing it as a generalized class of maps.

Findings

Identifies the properties of the chaotic transition in the CBM.

Shows the CBM cannot be reduced to the Hénon map via orthogonal transformations.

Highlights the CBM as a generalized Hénon map with unique features.

Abstract

Simplified models are a necessary steppingstone for understanding collective neural network dynamics, in particular the transitions between different kinds of behavior, whose universality can be captured by such models, without prejudice. One such model, the cortical branching model (CBM), has previously been used to characterize part of the universal behavior of neural network dynamics and also led to the discovery of a second, chaotic transition which has not yet been fully characterized. Here, we study the properties of this chaotic transition, that occurs in the mean-field approximation to the $k_{\sf in}=1$ CBM by focusing on the constraints the model imposes on initial conditions, parameters, and the imprint thereof on the Lyapunov spectrum. Although the model seems similar to the H\'enon map, we find that the H\'enon map cannot be recovered using orthogonal transformations to decouple the dynamics. Fundamental differences between the two, namely that the CBM is defined on a compact space and features a non-constant Jacobian, indicate that the CBM maps, more generally, represent a class of generalized H\'enon maps which has yet to be fully understood.