The computational role of structure in neural activity and connectivity

TL;DR

This paper presents a unified framework for analyzing neural activity and connectivity structures, linking geometric and modular properties to computational functions in neural networks.

Contribution

It introduces a general approach to characterize neural structures through representational geometry and modularity, connecting these to network computations.

Findings

Structures in neural activity can be characterized by geometry and modularity.

The framework reveals how these structures relate to computational roles.

Applied to model networks, it uncovers diverse structural patterns associated with different computations.

Abstract

One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help to understand the functional role of a particular brain area. Here we outline a unified approach to characterize these structures by inspecting the representational geometry and the modularity properties of the recorded activity, and show that this approach can also reveal structures in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent works on model networks performing three classes of computations.