Internal Feedback in Biological Control: Locality and System Level Synthesis

TL;DR

This paper proposes a novel theoretical framework using System Level Synthesis to explain internal feedback pathways in the brain, aligning with experimental data and emphasizing the importance of local processing and memory.

Contribution

It introduces a new theory based on optimal control principles that accounts for the prevalence of internal feedback pathways in neural systems.

Findings

Qualitative predictions match experimental observations

Introduction of a mesocircuit model for distributed processing

Highlights the need for extensive internal feedback and memory

Abstract

The presence of internal feedback pathways (IFPs) is a prevalent yet unexplained phenomenon in the brain. Motivated by experimental observations on 1) motor-related signals in visual areas, and 2) massively distributed processing in the brain, we approach this problem from a sensorimotor standpoint and make use of distributed optimal controllers to explain IFPs. We use the System Level Synthesis (SLS) controller to model neural phenomena such as signaling delay, local processing, and local reaction. Based on the SLS controller, we make qualitative predictions about IFPs that strongly align with existing experimental observations. We introduce a `mesocircuit' for optimal performance with distributed and local processing, and local disturbance rejection; this mesocircuit requires extreme amounts of IFPs and memory for proper function. This is the first theory that replicates the massive amounts of IFPs in the brain purely from a priori principles, providing a new theoretical basis upon which we can build to better understand the inner workings of the brain.