An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices

.This paper presents data-driven solutions to address two challenges in the problem of linking neural data and behavior: (1) unsupervised analysis of behavioral data and automatic label generation from behavioral observations, and (2) extraction of subject-invariant features for the development of g...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of neural engineering Jg. 21; H. 3
Hauptverfasser:	Salsabilian, Shiva, Lee, Christian, Margolis, David, Najafizadeh, Laleh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England IOP Publishing 01.06.2024
Schlagworte:	adversarial variational autoencoder Animals behavior Choice Behavior - physiology LSTM Mice Mice, Transgenic neural decoding Neural Networks, Computer self-supervised learning subject variability Supervised Machine Learning Unsupervised Machine Learning neural decoding self-supervised learning adversarial variational autoencoder LSTM behavior subject variability
ISSN:	1741-2560, 1741-2552, 1741-2552
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!