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title: 論文分析：Learnable latent embeddings for joint behavioural and neural analysis
tags: [論文分析]

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tags: 論文分析
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# 論文分析：Learnable latent embeddings for joint behavioural and neural analysis

# 摘要：
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.

# 想解決的問題
這篇論文旨在解決神經科學中的一個基本問題，即如何將行為動作與神經活動相互映射。隨著記錄大量神經和行為數據的能力增強，越來越多的人開始對建模自適應行為期間的神經動力學進行興趣，以探究神經表示。然而，目前缺乏一種能夠明確靈活地利用聯合行為和神經數據來揭示神經動態的非線性技術。因此，本文提出了一種新的編碼方法CEBRA，以聯合使用行為和神經數據的方式來生成一致且高性能的潛在空間，並展示了其在神經科學研究中的應用價值。

# 使用的方法
本文提出了一種新的編碼方法CEBRA，它以聯合使用行為和神經數據的方式來生成一致且高性能的潛在空間。該方法可以以監督式的假設或自我監督的發現驅動方式進行操作，並可用於解碼。此外，CEBRA還可以用於空間映射、解密自然視覺皮層的自然影片等其他應用。

# 最後的成果
本文的最終成果是提出了一種新的編碼方法CEBRA，它可以聯合使用行為和神經數據來生成一致且高性能的潛在空間。該方法可以用於解密自然視覺皮層的自然影片，並可以用於空間映射和揭示複雜的運動學特徵。此外，CEBRA還可以提供快速、高精度的解密自然影片的能力。

# 關鍵字
神經動力學、行為、編碼方法、潛在空間、解密。

