Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons

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.

• Publication year

  2025

• Venue

  Frontiers Comput. Neurosci.

• Publication date

  2025-08-13

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.3389/fncom.2025.1639829PMID 40880619PMCID 12380633
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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