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SUMMARY:The successor representation\, its neural substrate\, and behaviou
 ral consequences. - Puria Radmard\; Daniel Kornai
DTSTART:20250506T140000Z
DTEND:20250506T153000Z
UID:TALK231814@talks.cam.ac.uk
CONTACT:124819
DESCRIPTION:Breaking the mould of purely model free (MF) or model based (M
 B) reinforcement learning methods\, the successor representation (SR) (Day
 an\, 1993) is a unique factorisation of the value function that bridges MB
  and MF approaches. At the start of the talk\, Puria Radmard will discuss 
 the mathematical formalism behind the SR\, and provide a live demo of how 
 such a representation is iteratively learned. In the second part\, Daniel 
 Kornai will present two papers. In "The hippocampus as a predictive map" (
 Stachenfeld et. al 2017 Nature Neuroscience)\, the authors show how many p
 roperties of place fields and grid fields can be recapitulated by a model 
 that assumes that place cells encode the SR\, and grid cells encode a low 
 dimensional representation of the SR. In "The successor representation in 
 human reinforcement learning" (Momennejad et. al 2017 Nature Human Behavio
 ur)\, the authors show how human performance under continual reinforcement
  learning tasks is most consistent with a hybrid SR model. 
LOCATION:CBL Seminar Room\, Engineering Department\, 4th floor Baker build
 ing
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