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Data-driven models of neural and behavioural learning

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If you have a question about this talk, please contact Daniel Kornai .

Learning and adaptation play a central role in interacting with a dynamic environment. Neuroscience experiments have classically focused on how individual brain regions perform simplified tasks. However, recent technological advances have rapidly enabled the monitoring of large populations of neurons over many days, across multiple brain regions, and during increasingly complex behaviors. Yet even with such data within our reach, we still lack the theoretical and quantitative tools necessary to infer the fundamental principles guiding learning in the brain.

In this talk I will present several of our latest efforts to bridge this gap. First, by building state-dependent statistical models, we demonstrate that complex locomotor behaviours can be disentangled into a twofold learning process combining discrete and continuous aspects that are both refined over learning. Second, we propose a theoretical framework for how different motor regions (the motor cortex and cerebellum) could coordinate as a distributed learning system. Finally, I will present our recent development of tensor-based dimensionality reduction methods to track how neural dynamics change as learning unfolds. Together, our work aims to develop interpretable data-driven models to understand and link learning dynamics across neural and behavioural scales.

This talk is part of the Computational Neuroscience series.

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