Algebraic identification of the effective connectivity of constrained geometric network models of neural signaling

TL;DR

This paper introduces an algebraic method to determine the effective connectivity in constrained geometric neural network models, integrating structural and dynamical data for comprehensive network analysis.

Contribution

It presents a novel algebraic approach for calculating effective connectivity using network-level data and prior knowledge, applicable to various biological and non-biological networks.

Findings

Method effectively integrates structural and dynamical information.

Applicable to large and complex networks.

Provides a systematic way to infer effective connectivity.

Abstract

Cellular neural circuit and networks consisting of interconnected neurons and glia are ulti- mately responsible for the information processing associated with information processing in the brain. While there are major efforts aimed at mapping the structural and (electro)physiological connectivity of brain networks, such as the White House BRAIN Initiative aimed at the devel- opment of neurotechnologies capable of high density neural recordings, theoretical and compu- tational methods for analyzing and making sense of all this data seem to be further behind. Here, we propose and provide a summary of an approach for calculating effective connectivity from experimental observations of neuronal network activity. The proposed method operates on network-level data, makes use of all relevant prior knowledge, such as dynamical models of individual cells in the network and the physical structural connectivity of the network, and is broadly applicable to large classes of biological and non-biological networks.