METHODS article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1639829
Maximum Likelihood Estimation of Spatially Dependent Interactions in Large Populations of Cortical Neurons
Provisionally accepted
- School of Psychology, University of Ottawa, Ottawa, Canada
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Understanding how functional connectivity between cortical neurons varies with spatial distance is crucial for characterizing large-scale neural dynamics. However, inferring these spatial patterns is challenging when spike trains are collected from large populations of neurons. Here, we present a maximum likelihood estimation (MLE) framework to quantify distance-dependent functional interactions directly from observed spiking activity. We validate this method using both synthetic spike trains generated from a linear Poisson model and biologically realistic simulations performed with Izhikevich neurons. We then apply the approach to large-scale electrophysiological recordings from V1 cortical neurons. Our results show that the proposed MLE approach robustly captures spatial decay in functional connectivity, providing insights into the spatial structure of populationlevel neural interactions.
Keywords: Distance dependent connectivity, maximum likelihood, Visual cortex (V1), calcium imaging, spiking neuron model
Received: 02 Jun 2025; Accepted: 22 Jul 2025.
Copyright: © 2025 Godin and Thivierge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jean-Philippe Thivierge, School of Psychology, University of Ottawa, Ottawa, Canada
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