Hosted by the Wellcome Neural Dynamics PhD Programme
Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures complex temporal and highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. We applied Rastermap to a variety of datasets, including spontaneous neural activity, neural activity during a virtual reality task, widefield neural imaging data from a 2AFC task, and artificial neural activity from an agent playing atari games. We found within these datasets unique subpopulations of neurons encoding abstract elements of decision-making, the environment and behavioral states. To interrogate behavioral representations in the mouse brain, we developed a fast deep-learning model for tracking 13 distinct points on the mouse face recorded from arbitrary camera angles. The model was just as accurate as state-of-the-art pose estimation tools while being several times faster, making it a powerful tool for closed-loop behavioral experiments. Next, we aligned facial key points across mice in order to train a universal model to predict neural activity from behavior. The universal mouse model could predict neural activity as well as a model fit to a single mouse, showing that neural representations of behaviors are conserved across mice. The latent states extracted from the universal model contained interpretable mouse behaviours.
Online: https://bristol-ac-uk.zoom.us/j/97134251766?pwd=YkFhcHlkNHY0RSt2T3pJRHVrbUt5QT09
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