Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework.

ABSTRACT: The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present the Similarity Networks (SIMNETS) analysis framework: an efficient and scalable pipeline designed to embed simultaneously recorded neurons into low dimensional maps according to the intrinsic relationship between their spike trains, making it possible to identify and visualize groups of neurons performing similar computations. The critical innovation is the use of pairwise spike train similarity (SSIM) matrices to capture th

SUBMITTER: Vargas-Irwin CE 

PROVIDER: S-EPMC12391057 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature