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

作     者：Po, Ho Fai Houben, Akke Mats Haeb, Anna-Christina Jenkins, David Rhys Hill, Eric J. Parri, H. Rheinallt Soriano, Jordi Saad, David 

作者机构：UNIV ASTON DEPT PHARM BIRMINGHAM B4 7ET England Univ Barcelona Dept Fis Mat Condensada E-08028 Barcelona Spain Univ Barcelona Inst Complex Syst UBICS Barcelona 08028 Spain Aston Univ Coll Hlth & Life Sci Birmingham B4 7ET England Aston Univ Aston Inst Membrane Excellence Birmingham B4 7ET England Loughborough Univ Dept Chem Loughborough LE11 3TU Leics England 

出 版 物：《PNAS NEXUS》 (PNAS Nexus)

年 卷 期：2025年第4卷第1期

页      面：pgae565页

基　　金：European Union Horizon 2020 research and innovation program MCIU/AEI [PID2022-137713NB-C22, PLEC2022-009401] ERDF/EU Generalitat de Catalunya [2021-SGR-00450] 

主　　题：biological neuronal networks inference neuronal-type classification kinetic Ising model generalized maximum likelihood expectation-maximization algorithms 

摘      要：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.