Markov Blankets in the Brain

TL;DR

This paper explores how Markov blankets can be used to analyze neural system partitions across multiple scales, revealing limitations of traditional modular brain models.

Contribution

It introduces a framework applying Markov blankets to brain architectures at various levels, linking empirical micro-circuitry with system-wide analysis.

Findings

Applicable to neurons, regions, and networks

Highlights limitations of single-level modular views

Connects empirical micro-circuitry with system analysis

Abstract

Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. This depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level. The nuance afforded by partitioning neural systems in this way highlights certain limitations of modular perspectives of brain function that only consider a single level of description.