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 language | English (US) |
|---|---|
| Article number | e1005141 |
| Journal | PLoS Computational Biology |
| Volume | 12 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2016 |
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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
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 journal › Article
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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.
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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 -