Extracting latent structure from multiple interacting neural populations

João D. Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu

Research output: Contribution to journalConference article

12 Scopus citations

Abstract

Developments in neural recording technology are rapidly enabling the recording of populations of neurons in multiple brain areas simultaneously, as well as the identification of the types of neurons being recorded (e.g., excitatory vs. inhibitory). There is a growing need for statistical methods to study the interaction among multiple, labeled populations of neurons. Rather than attempting to identify direct interactions between neurons (where the number of interactions grows with the number of neurons squared), we propose to extract a smaller number of latent variables from each population and study how these latent variables interact. Specifically, we propose extensions to probabilistic canonical correlation analysis (pCCA) to capture the temporal structure of the latent variables, as well as to distinguish within-population dynamics from between-population interactions (termed Group Latent Auto-Regressive Analysis, gLARA). We then applied these methods to populations of neurons recorded simultaneously in visual areas V1 and V2, and found that gLARA provides a better description of the recordings than pCCA. This work provides a foundation for studying how multiple populations of neurons interact and how this interaction supports brain function.

Original languageEnglish (US)
Pages (from-to)2942-2950
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume4
Issue numberJanuary
StatePublished - Jan 1 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: Dec 8 2014Dec 13 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Semedo, J. D., Zandvakili, A., Kohn, A., Machens, C. K., & Yu, B. M. (2014). Extracting latent structure from multiple interacting neural populations. Advances in Neural Information Processing Systems, 4(January), 2942-2950.