Inferring Structure of Cortical Neuronal Networks from Firing Data: A Statistical Physics Approach

TL;DR

This paper introduces a probabilistic method combining Bayesian statistics, statistical physics, and machine learning to infer the structure and function of cortical neuronal networks from activity data, aiding understanding of neural plasticity and learning.

Contribution

The paper presents a novel integrated approach for inferring effective neuronal network structures and their excitatory or inhibitory nature from firing data, outperforming existing methods.

Findings

Accurately infers network structure from synthetic and real data.

Predicts neuronal spiking activity effectively.

Outperforms existing inference methods.

Abstract

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory nature of its constituents, and predict neuronal spiking activities by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.