Episodic memories are highly dynamic and change in nonlinear ways over time. This dynamism is not captured by existing systems consolidation theories …

While the thalamus is known to relay and modulate sensory signals to the cortex, whether it also participates in active computation and intrinsic signal generation remains unresolved. The anterodorsal...

MARBLE uses geometric deep learning to map dynamics such as neural activity into a latent representation, which can then be used to decode the neural activity or compare it across systems.

idTracker and idtracker.ai approach multi-animal tracking from video as an image classification problem. For this classification, both rely on segments of video where all animals are visible to extract images and their identity labels. When these segments are too short, tracking can become slow and inaccurate and, if they are absent, tracking is impossible. Here, we introduce a new idtracker.ai that reframes multi-animal tracking as a representation learning problem rather than a classification task. Specifically, we apply contrastive learning to image pairs that, based on video structure, are known to belong to the same or different identities. This approach maps animal images into a representation space where they cluster by animal identity. As a result, the new idtracker.ai eliminates the need for video segments with all animals visible, is more accurate, and tracks up to 440 times faster.

Neuroethology, the study of the neural mechanisms underlying natural behaviours, has faced challenges due to the complexity of behaviours, limitations in genetic manipulation, and recording constrai…