Uncovering motifs of concurrent signaling across multiple neuronal populations

Evren Gokcen, Anna I. Jasper, Alison Xu, Adam Kohn, Christian K. Machens, Byron M. Yu

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Modern recording techniques now allow us to record from distinct neuronal populations in different brain networks.However, especially as we consider multiple (more than two) populations, new conceptual and statistical frameworks are needed to characterize the multi-dimensional, concurrent flow of signals among these populations.Here, we develop a dimensionality reduction framework that determines (1) the subset of populations described by each latent dimension, (2) the direction of signal flow among those populations, and (3) how those signals evolve over time within and across experimental trials.We illustrate these features in simulation, and further validate the method by applying it to previously studied recordings from neuronal populations in macaque visual areas V1 and V2.Then we study interactions across select laminar compartments of areas V1, V2, and V3d, recorded simultaneously with multiple Neuropixels probes.Our approach uncovered signatures of selective communication across these three areas that related to their retinotopic alignment.This work advances the study of concurrent signaling across multiple neuronal populations.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
Volume36
StatePublished - 2023
Event37th Conference on Neural Information Processing Systems, NeurIPS 2023 - New Orleans, United States
Duration: Dec 10 2023Dec 16 2023

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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