Uncovering motifs of concurrent signaling across multiple neuronal populations

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

Keywords: neuroscience, multi-population neural recordings, dimensionality reduction, latent variable models, Gaussian processes

TL;DR: We developed a dimensionality reduction framework for characterizing the multi-dimensional, concurrent flow of signals across multiple neuronal populations.

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.

Submission Number: 15007