Inferring structure of cortical neuronal networks from activity data: A statistical physics approach

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 type of its constituents, and predict neuronal spiking activity 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.

Publication DOI: https://doi.org/10.1093/pnasnexus/pgae565
Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied Mathematics & Data Science
College of Health & Life Sciences > School of Biosciences
College of Health & Life Sciences > Aston Pharmacy School
Funding Information: This research is supported by the European Union Horizon 2020 research and innovation program under Grant No. 964877 (project NEU-CHiP). J.S. also acknowledges support from grants PID2022-137713NB-C22 and PLEC2022-009401, funded by MCIU/AEI/10.13039/50110
Additional Information: Copyright © The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: biological neuronal networks inference, neuronal-type classification, kinetic Ising model, generalized maximum likelihood, expectation–maximization algorithms
Publication ISSN: 2752-6542
Data Access Statement: All data presented in this paper are available from https://doi.org/10.17036/researchdata.aston.ac.uk.00000635. This includes the<br/>Python file containing the derived algorithm, as well as both thein silico and in vitro neuronal firing data being studied.
Last Modified: 01 Apr 2025 07:11
Date Deposited: 10 Jan 2025 18:51
Full Text Link:
Related URLs: https://academi ... pgae565/7928818 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2025-01
Published Online Date: 2024-12-19
Accepted Date: 2024-12-11
Authors: Po, Ho Fai (ORCID Profile 0000-0002-3056-4064)
Houben, Akke Mats
Haeb, Anna-Christina
Jenkins, David Rhys
Hill, Eric J. (ORCID Profile 0000-0002-9419-1500)
Parri, H. Rheinallt (ORCID Profile 0000-0002-1412-2688)
Soriano, Jordi
Saad, David (ORCID Profile 0000-0001-9821-2623)

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