Inferring the temporal evolution of synaptic weights from dynamic functional connectivity

Brain Informatics

Research

Stefano Panzeri¹  Marco Celotto²  Stefan Lemke³ 

[1] Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany;Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy;Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany;Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy;Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy;Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy;Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, USA;

关键词: Dynamic functional connectivity;    Spiking neural network;    Communication delay;    Transfer entropy;    Cross-covariance;   

DOI  :  10.1186/s40708-022-00178-0

 received in 2022-10-06, accepted in 2022-11-14,  发布年份 2022

来源: Springer

【 摘 要 】

How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.

【 授权许可】

CC BY   
© The Author(s) 2022

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