Event:

Modern neurotechnologies generate high-resolution maps of the brain-wide neural activity and anatomical connectivity. However, theoretical frameworks are missing to explain how global activity arises from connectivity to drive animal behaviors. I will present our recent work developing models of global neural dynamics, which utilize anatomical connectivity and predict behavioral outputs. First, we took advantage of recently available large-scale datasets of neural activity and connectivity to construct a model of mesoscopic functional dynamics across the mouse cortex. We found that global activity is restricted to a low-dimensional subspace spanned by a few cortical areas and explores different parts of this subspace in different behavioral contexts. Our model provides an interpretable dimensionality reduction of cortex-wide neural dynamics grounded on the connectome, which generalizes across animals and behaviors. Second, we developed a computational approach for inferring interpretable neural circuits from high-dimensional neural response data. We model neural responses as a high-dimensional embedding of low-dimensional trajectories generated by a latent circuit, whereby we simultaneously infer the embedding and latent circuit connectivity. By integrating activity and connectivity, our computational frameworks generate testable hypotheses for perturbation experiments.