figshare
Browse
journal.pcbi.1005141.pdf (5.75 MB)

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

Download (5.75 MB)
journal contribution
posted on 2016-12-07, 00:00 authored by Ryan C. Williamson, Benjamin Crowley, Ashok Litwin-Kumar, Brent Doiron, Adam Kohn, Matthew A. Smith, Byron YuByron Yu
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.

History

Publisher Statement

This is the Published PDF version of, "Williamson RC, Cowley BR, Litwin-Kumar A, Doiron B, Kohn A, Smith MA, et al. (2016) Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. PLoS Comput Biol 12(12): e1005141. https://doi.org/10.1371/journal.pcbi.1005141."

Date

2016-12-07

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC