A large-scale neural network training framework for generalized estimation of single-trial population dynamics.
Nat Methods
; 19(12): 1572-1577, 2022 12.
Article
en En
| MEDLINE
| ID: mdl-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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Corteza Motora
Límite:
Animals
Idioma:
En
Revista:
Nat Methods
Asunto de la revista:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos