Arif Hamid - Spatiotemporal dopaminergic mechanisms for flexible behaviors and agency learning

Event Details:

Thursday, May 5, 2022
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Time
12:00pm to 1:00pm PDT
Location
Contacts
neuroscience@stanford.edu
Event Sponsor
Wu Tsai Neurosciences Institute
Hosted by Mari Sosa (Giocomo Lab)
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Wu Tsai Neuro's weekly seminar series is being held virtually during the spring quarter. We hope to be able to bring the community together for in-person seminars again in the fall.

Community members interested in meeting with this week's speaker should contact host Mari Sosa of the Giocomo Lab.

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Arif Hamid

Hanna Gray Fellow at HHMI, and Assistant Professor of Neuroscience and Medical Discovery Team in Addiction

University of Minnesota

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Dr. Hamid is a Hanna Gray Fellow at HHMI, and Assistant Professor of Neuroscience and Medical Discovery Team in Addiction (MDTA) at the University of Minnesota. He received doctoral training with Joshua Berke at University of Michigan, and completed postdoctoral training at Brown University with Michael Frank and Christopher Moore.

Dr. Hamid's research group aims to deeply understanding brain mechanisms for flexible behavioral-control, with a particular focus on the role of the neurotransmitter dopamine and Cortico-Basal ganglia circuit in reinforcement learning (RL). They combine interdisciplinary approaches to (i) study the precise anatomical, temporal and functional properties of brain decision-networks, (ii) and link these biological circuit mechanisms to specific computational operations under normative theoretical frameworks,  (iii) to ultimately understand how these circuit and computational specializations become leveraged during various behavioral demands.

Spatiotemporal dopaminergic mechanisms for flexible behaviors and agency learning

Abstract

In this talk, I will explore computational motivations and empirical evidence for regionally specialized dopamine (DA) signals for learning and performance in fronto-striatal networks. I will focus on cognitive dorsomedial striatum as a case study to illustrate that regional DA signals are specialized to local functional specializations, in stark contrast to the standing view of globally broadcast DA error signals.