![]() ![]() Current tools for representation learning are either linear or, if nonlinear, typically rely on generative models and they do not yield consistent embeddings across animals (or repeated runs of the algorithm). Therefore, we need new methods that can combine data across animals and sessions with minimal assumptions, thereby generating interpretable neural embedding spaces 1, 3. Nevertheless, we are still limited in both the number of neurons and length of time we can record from behaving animals in a session. Similar content being viewed by othersĪ central quest in neuroscience is the neural origin of behaviour 1, 2. 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. It allows leverage of single- and multi-session datasets for hypothesis testing or can be used label free. ![]() ![]() 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. We show that consistency can be used as a metric for uncovering meaningful differences, and the inferred latents can be used for decoding. 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. 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 dynamics 3, 4, 5. 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 representations 1, 2, 3. ![]() Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. ![]()
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