Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Seely, J. S., Kaufman, M. T., Ryu, S. I., Shenoy, K. V., Cunningham, J. P., Churchland, M. M. (November 2016) Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1. PLoS Comput Biol, 12 (11). e1005164. ISSN 1553-7358 (Electronic)1553-734X (Linking)

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URL: https://www.ncbi.nlm.nih.gov/pubmed/27814353
DOI: 10.1371/journal.pcbi.1005164

Abstract

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.

Item Type: Paper
Subjects: organism description > animal behavior
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits

organs, tissues, organelles, cell types and functions > tissues types and functions > visual cortex
CSHL Authors:
Communities: CSHL labs > Churchland lab
Depositing User: Matt Covey
Date: 4 November 2016
Date Deposited: 15 Nov 2016 16:20
Last Modified: 13 Feb 2017 21:14
PMCID: PMC5096707
Related URLs:
URI: http://repository.cshl.edu/id/eprint/33890

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