2023-05-04 スイス連邦工科大学ローザンヌ校(EPFL)
◆このアルゴリズムは、脳信号と行動ラベル(測定された動き、「報酬」などの抽象的なラベル、画像の色や質感などの感覚的特徴など)を対応させることによって、神経コードの隠れた構造を学習する。CEBRAは、視覚野の1%未満のニューロンで良好な性能を発揮し、マウスが映画を見ているときに見ているものを復元したり、霊長類の腕の動きを予測したり、ラットが闘技場を自由に走り回っているときの位置を復元することができます。
<関連情報>
- https://actu.epfl.ch/news/predict-what-a-mouse-sees-by-decoding-brain-signal/
- https://www.nature.com/articles/s41586-023-06031-6
行動と神経の共同解析のための学習可能な潜在埋込量 Learnable latent embeddings for joint behavioural and neural analysis
Steffen Schneider,Jin Hwa Lee & Mackenzie Weygandt Mathis
Nature Published:03 May 2023
DOI:https://doi.org/10.1038/s41586-023-06031-6
Abstract
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural representations1,2,3. In particular, although neural latent embeddings can reveal underlying correlates of behaviour, we lack nonlinear techniques that can explicitly and flexibly leverage joint behaviour and neural data to uncover neural dynamics3,4,5. Here, we fill this gap with a new encoding method, CEBRA, that jointly uses behavioural and neural data in a (supervised) hypothesis- or (self-supervised) discovery-driven manner to produce both consistent and high-performance latent spaces. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding. We validate its accuracy and demonstrate our tool’s utility for both calcium and electrophysiology datasets, across sensory and motor tasks and in simple or complex behaviours across species. It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free. Lastly, we show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, for the production of consistent latent spaces across two-photon and Neuropixels data, and can provide rapid, high-accuracy decoding of natural videos from visual cortex.
