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

In Nature methods ; h5-index 152.0

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.

Keshtkaran Mohammad Reza, Sedler Andrew R, Chowdhury Raeed H, Tandon Raghav, Basrai Diya, Nguyen Sarah L, Sohn Hansem, Jazayeri Mehrdad, Miller Lee E, Pandarinath Chethan

2022-Nov-28