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SUMMARY:Striatal dopamine computations in learning about agency - Prof Mic
 hael Frank\, Brown University
DTSTART:20210429T113000Z
DTEND:20210429T123000Z
UID:TALK156259@talks.cam.ac.uk
CONTACT:87079
DESCRIPTION:*Abstract:* The basal ganglia and dopaminergic systems are wel
 l studied for their roles in reinforcement learning and reward-based decis
 ion making.   Much work focuses on "reward prediction error"  (RPE) signal
 s conveyed by dopamine and used for learning. Computational considerations
  suggest that such signals may be enriched beyond the classical global and
  scalar RPE computation\, to support more structured learning in distinct 
 sub-circuits ("vector RPEs").   Such signals allow an agent to assign cred
 it to the level of action selection most likely responsible for the outcom
 es\, and hence to enhance learning depending on the generative task statis
 tics. Experimental data from mice will be presented\,  showing spatiotempo
 ral dynamics of dopamine terminal activity and release across the dorsal s
 triatum in the form of traveling waves that support learning about agency.
 \n\n*Biography:* Prof Frank received his Ph.D. in Neuroscience & Psycholog
 y (joint) at the University of Colorado at Boulder in 2004. After working 
 as a Professor at the University of Arizona\, he joined the Brown Universi
 ty’s Cognitive\, Linguistic & Psychological Science department in 2011\,
  where he is currently an Edgar L. Marston Professor. His research combine
 s multiple levels of computational modelling and experimental work to unde
 rstand the neural mechanisms underlying reinforcement learning\, decision 
 making and cognitive control. His lab also develops neural circuit and alg
 orithmic models of systems-level interactions between multiple brain areas
  (primarily prefrontal cortex and basal ganglia and their modulation by do
 pamine). For detailed biography of Prof Frank\, please visit: http://ski.
 cog.brown.edu/\n
LOCATION: Webinar  (via Zoom online)
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