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Neural representations of learning and behavior
A fundamental question in neuroscience is how neural populations learn to control flexible behaviors. A promising region for understanding the relationship between neural circuitry, population activity, and behavior is the cerebellum, whose evolutionarily-conserved circuitry is the basis of a critical role in motor learning guided by sensory errors. In the first part of this talk, I will present our recent work attempting to understand how cerebellar supervised learning can guide motor learning and adaptation in coordination with recurrent cortical dynamics. Furthermore, testing such theories of population-level learning in data requires methods that can infer how neural dynamics evolve over slow timescales. In the second part of the talk, I will present our recent development of low tensor rank recurrent neural networks, which can identify how latent neural dynamics are reshaped over learning from high-dimensional neural data.
Date:
25 April 2024, 14:30
Venue:
Sherrington Library, off Parks Road OX1 3PT
Speaker:
Alex Cayco-Gajic (École Normale Supérieure Paris)
Organising department:
Medical Sciences Division
Organiser:
Dr Rui Ponte Costa (University of Oxford)
Part of:
Oxford NeuroAI Forum
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Rui Costa
