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portfolio
Data-driven models for control of biomechanical bodies
Closed-loop neural controllers with biologically grounded sensory feedback for realistic locomotion in Drosophila.
Population dynamics in the cerebellar cortex
Network structure shapes inhibitory network dynamics and representational transformations across cerebellar circuits.
Dynamical constraints on learning
How feedback, intrinsic dynamics, and controllability constrain learning timescales for fast adaptation.
Learning without plasticity
Fast adaptation via input-driven reorganization of dynamics, contextual inference, and flexible inter-area associations.
Role of perceptual uncertainty in reward-driven learning
Computational signatures of reinforcement learning when state inference is uncertain due to noisy perception.
Data-driven discovery of shared latent dynamics
Identifying nonlinear dynamics from data, and aligning neural population activity across sessions.
publications
Dopaminergic and prefrontal basis of learning from sensory confidence and reward value.
Published in Neuron, 2020
Recommended citation: https://doi.org/10.1016/j.neuron.2019.11.018
Multidimensional population activity in an electrically coupled inhibitory circuit in the cerebellar cortex
Published in Neuron, 2021
Recommended citation: https://doi.org/10.1016/j.neuron.2021.03.027
Cerebellar granule cell axons support high-dimensional representations
Published in Nature Neuroscience, 2021
Recommended citation: https://www.nature.com/articles/s41593-021-00873-x
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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