Fate of Duplicated Neural Structures

TL;DR

This paper explores how statistical mechanics can predict the conditions under which neural circuits duplicate or remain singular, revealing phase transitions that influence brain evolution and complexity.

Contribution

It introduces a formal statistical physics framework to analyze the fate of duplicated neural structures and their role in brain evolution.

Findings

Phase diagrams show conditions favoring neural duplication or singularity.

Transitions between single and duplicated circuits constrain evolutionary pathways.

The framework suggests universal principles governing neural circuit organization.

Abstract

Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical mechanics, computation, and (through their role in cognition) natural selection. Can we establish a {\em statistical physics} of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with a stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this we derive {\em phase diagrams} and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical mechanics seems a natural framework for thsi worthy line of research.