A large-scale neural network training framework for generalized estimation of single-trial population dynamics.

ABSTRACT: Achieving state-of-the-art performance with deep neural population dynamics models requires extensive hyperparameter tuning for each dataset. AutoLFADS is a model-tuning framework that automatically produces high-performing autoencoding models on data from a variety of brain areas and tasks, without behavioral or task information. We demonstrate its broad applicability on several rhesus macaque datasets: from motor cortex during free-paced reaching, somatosensory cortex during reaching with perturbations, and dorsomedial frontal cortex during a cognitive timing task.

SUBMITTER: Keshtkaran MR 

PROVIDER: S-EPMC9825111 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature