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 propert

SUBMITTER: Williamson RC 

PROVIDER: S-EPMC5142778 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature