Inference of topology and the nature of synapses, and the flow of information in neuronal networks

TL;DR

This paper introduces a method using causal mutual information to accurately infer neuronal connectivity, synapse nature, and information flow directionality from time-series data, even with noise and limited measurements.

Contribution

The work demonstrates that causal mutual information can distinguish excitatory and inhibitory synapses and determine their directionality in neuronal networks, advancing connectivity analysis.

Findings

Non-adjacent neurons can 'understand' each other via positive mutual information.

Inhibitory connections transfer more information than excitatory ones.

Method remains effective under Gaussian noise and with limited mean field data.

Abstract

The characterisation of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time-series. The success of our approach relies on a surprising property found in neuronal networks by which non-adjacent neurons do "understand" each other (positive mutual information), however this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronisation, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of inter modular connectivity, when only mean fields can be measured.