| Autor: |
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 |
| Zdroj: |
Nature Methods; December 2022, Vol. 19 Issue: 12 p1572-1577, 6p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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