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

Journal: Nature methods
PMID: 36443486

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.

Authors

  • Mohammad Reza Keshtkaran
    Department of Electrical and Computer Engineering, National University of Singapore, 117583, Singapore. Department of Ophthalmology, National University of Singapore, 117583, Singapore. Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States of America.
  • Andrew R Sedler
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
  • Raeed H Chowdhury
    Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
  • Raghav Tandon
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
  • Diya Basrai
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
  • Sarah L Nguyen
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
  • Hansem Sohn
    Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Mehrdad Jazayeri
    McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: mjaz@mit.edu.
  • Lee E Miller
    Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
  • Chethan Pandarinath
    Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.

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