Layers, Folds, and Semi-Neuronal Information Processing

TL;DR

This paper explores how layered and folded phenotypic structures in embodied agents influence information processing and self-regulation, using formal models and toy simulations to understand biological and cognitive functions.

Contribution

It introduces a formal meta-brain model demonstrating how layering and folding contribute to phenotypic information processing and self-regulation in biological and simulated systems.

Findings

Layered phenotypic representations can process information and self-regulate.

Folding in neural networks can cause distortions with functional implications.

Meta-brain models aid in understanding biological cognition and enactivism.

Abstract

What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation.