Matthew Chalk

Wednesday 27th November 2019

Time:4pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

Inferring the function performed by a recurrent neural network

Neural circuits perform a diverse range of functions, from encoding sensory signals to storing memories and controlling muscle movements. A long-standing challenge is to work out which functions are performed by different circuits, and how. I will argue that a well-established framework (inverse reinforcement learning) for estimating an agent's goals, such as an animal looking for food or a person driving to work, could be used to infer the function performed by a recorded neural circuit. This framework could help reveal the various computations neural circuits have evolved to perform. Further, it predicts how neural responses should adapt to perform the same function under varying conditions (e.g.~different stimuli/cell death).

Bio:
Matthew Chalk homepage https://matthewjchalk.wixsite.com/mysite