Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

Ryan C. Williamson, Benjamin R. Cowley, Ashok Litwin-Kumar, Brent Doiron, Adam Kohn, Matthew A. Smith, Byron M. Yu

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

Original languageEnglish (US)
Article numbere1005141
JournalPLoS Computational Biology
Volume12
Issue number12
DOIs
StatePublished - Dec 1 2016

Fingerprint

Neural Networks (Computer)
Dimensionality Reduction
Population Model
Network Model
Neurons
Neuron
neurons
Scaling
Network Structure
Population
Dimensionality
Output
primate
factor analysis
Visual Cortex
Generalise
connectivity
brain
Factor analysis
Factor Analysis

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Williamson, R. C., Cowley, B. R., Litwin-Kumar, A., Doiron, B., Kohn, A., Smith, M. A., & Yu, B. M. (2016). Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. PLoS Computational Biology, 12(12), [e1005141]. https://doi.org/10.1371/journal.pcbi.1005141

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. / Williamson, Ryan C.; Cowley, Benjamin R.; Litwin-Kumar, Ashok; Doiron, Brent; Kohn, Adam; Smith, Matthew A.; Yu, Byron M.

In: PLoS Computational Biology, Vol. 12, No. 12, e1005141, 01.12.2016.

Research output: Contribution to journalArticle

Williamson, RC, Cowley, BR, Litwin-Kumar, A, Doiron, B, Kohn, A, Smith, MA & Yu, BM 2016, 'Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models', PLoS Computational Biology, vol. 12, no. 12, e1005141. https://doi.org/10.1371/journal.pcbi.1005141
Williamson, Ryan C. ; Cowley, Benjamin R. ; Litwin-Kumar, Ashok ; Doiron, Brent ; Kohn, Adam ; Smith, Matthew A. ; Yu, Byron M. / Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. In: PLoS Computational Biology. 2016 ; Vol. 12, No. 12.
@article{6314a14d9f304e0e9ce0451a4e443760,
title = "Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models",
abstract = "Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.",
author = "Williamson, {Ryan C.} and Cowley, {Benjamin R.} and Ashok Litwin-Kumar and Brent Doiron and Adam Kohn and Smith, {Matthew A.} and Yu, {Byron M.}",
year = "2016",
month = "12",
day = "1",
doi = "10.1371/journal.pcbi.1005141",
language = "English (US)",
volume = "12",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

TY - JOUR

T1 - Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

AU - Williamson, Ryan C.

AU - Cowley, Benjamin R.

AU - Litwin-Kumar, Ashok

AU - Doiron, Brent

AU - Kohn, Adam

AU - Smith, Matthew A.

AU - Yu, Byron M.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

AB - Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

UR - http://www.scopus.com/inward/record.url?scp=85007575561&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007575561&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1005141

DO - 10.1371/journal.pcbi.1005141

M3 - Article

VL - 12

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 12

M1 - e1005141

ER -