Brief Bio:
I am a Gray Fellow at Howard Hughes Medical Institute and postdoctoral fellow in the labs of Christopher I. Moore and Micheal J. Frank at the Department of Neuroscience, Brown University. My PhD training was with Joshua D. Berke (now UCSF) at the University of Michigan. I received a BS in Neuroscience from University of Minnesota. Research: My interests are in understanding brain computations for flexible behavioral-control and learning, and I have especially focused on the neurotransmitter dopamine and Cortico-Basal Ganglia mechanisms for reward-learning and performance. I am specifically trained to combine multiple, interdisciplinary approaches to study the precise anatomical, temporal and functional properties of brain decision-circuits, and link these circuit mechanisms to specific computational operations that can be leveraged during different behavioral demands under normative theoretical frameworks. I have vast expertise in sophisticated rodent behavioral approaches, together with a variety of methods for neural activity assay and control (e.g. high-density electrophysiology, in vivo neurotransmitter measurement, awake and behaving one and two-photon microscopy together with neural control). I am also continuing to receive additional training in formalizing theories that integrate my experimental findings into multilevel neurocomputational models for tandem and cyclical advances in modeling and empirical work. My previous scientific contributions (see below or CV) have reported novel empirical findings that have significantly (re)shaped computational formalizations of dopamine’s functions in Reinforcement Learning (RL) frameworks, providing complementary advances in theoretical and mechanistic understanding of reward learning. For example:
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Research Goals:
-Identify circuit mechanisms that facilitate dopaminergic computations | Empirical studies
- Novel DA waves in the striatum
- "bottom up" and "top down" DA regulation
- Multiple timescales of spatiotemporal DA dynamics
-What are the computational operations implemented? | Quantitative/Computational description
-How do behavioral demands leverage these circuit/computational specializations? | Behavior + Theory + Neuroscience
Specific Projects:
What decision-signal(s) are relayed by DA?
Computational functions, behavioral consequences
Computational functions, behavioral consequences
- see Hamid et. al., NatNeuro 2016;
- Mohebi et. al., Nautre 2019;
- Hamid et. al., Bioarxv 2019;
- Hamid and Berke, In Prep
How are brain-wide DA decision-signals orchestrated?
Organizational principles
Organizational principles
What is the relationship between rapid [DA] and [DA] tone?
How does this relationship influence decision-making?
How does this relationship influence decision-making?
- see Hamid et. al., NatNeuro 2016;
- Hamid et. al., In Prep
Integration of novel empirical observations into existing or refined
normative frameworks that are more accurate, and biologically plausible.
normative frameworks that are more accurate, and biologically plausible.
Dopamine-Vasculature interactions in health and disease.
Cortical and BG circuit dynamics during flexible behaviors.
Cortical and BG circuit dynamics during flexible behaviors.