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

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science, [2005]-

Models, Neurological*; Brain Mapping/*methods ; Motor Cortex/*physiology ; Movement/*physiology ; Visual Cortex/*physiology ; Visual Perception/*physiology; Animals ; Computer Simulation ; Diffusion Tensor Imaging/methods ; Haplorhini ; Nerve Net/physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity

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.
Competing Interests: The authors have declared that no competing interests exist.

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T32 HD007430 United States HD NICHD NIH HHS; R01 EY016774 United States EY NEI NIH HHS; R01 NS076460 United States NS NINDS NIH HHS; United Kingdom WT_ Wellcome Trust; DP2 NS083037 United States NS NINDS NIH HHS; DP1 HD075623 United States HD NICHD NIH HHS

Date Created: 20161105 Date Completed: 20170530 Latest Revision: 20240607

20240607

PMC5096707

10.1371/journal.pcbi.1005164

27814353