{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:31:25Z","timestamp":1755225085121,"version":"3.43.0"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fncom.2025.1639829","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T05:32:50Z","timestamp":1755063170000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons"],"prefix":"10.3389","volume":"19","author":[{"given":"Camille","family":"Godin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. 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