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...

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Bibliographic Details
Published in:	Journal of neural engineering Vol. 21; no. 3
Main Authors:	Salsabilian, Shiva, Lee, Christian, Margolis, David, Najafizadeh, Laleh
Format:	Journal Article
Language:	English
Published:	England IOP Publishing 01.06.2024
Subjects:	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 Access:	Get full text
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