ArticleFrontiers in computational neuroscience2025
Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.
Article in Frontiers in computational neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 population-level neural interactions.
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