Dimensionality reduction beyond neural subspaces with slice tensor component analysis.

ABSTRACT: Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and rece

SUBMITTER: Pellegrino A 

PROVIDER: S-EPMC11537991 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature