Brain dynamics like reconstructing a viewed video uncovered using a machine-learning algorithm

Amazing stuff! More to come!

From the 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 representations. 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. 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."

Brain dynamics uncovered using a machine-learning algorithm (no public access. This is funny: This summary is behind a paywall, but the respective research article itself is open access.) CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode activity from the visual cortex of the mouse brain to reconstruct a viewed video.

Learnable latent embeddings for joint behavioural and neural analysis (open access) 

Credits: Last Week in AI

Fig. 1: Use of CEBRA for consistent and interpretable embeddings.

Fig. 3: Forelimb movement behaviour in a primate.

Fig. 5: Decoding of natural video features from mouse visual cortical areas.