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PLoS Comput Biol. 2016 Dec 9;12(12):e1005185. doi: 10.1371/journal.pcbi.1005185. eCollection 2016 Dec.

Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex.

Cowley BR1,2, Smith MA2,3,4,5, Kohn A6,7, Yu BM2,8,9.

Author information

1
Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
2
Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
3
Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
4
Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
5
Fox Center for Vision Restoration, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
6
Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America.
7
Department of Ophthalmology and Vision Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America.
8
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
9
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Abstract

Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus.

PMID:
27935935
PMCID:
PMC5147778
DOI:
10.1371/journal.pcbi.1005185
[Indexed for MEDLINE]
Free PMC Article

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