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Mission and Values

We strive to:​

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  1. Tackle the most foundational mysteries of the mind through INNOVATIVE, COLLABORATIVE, and OPEN science with the highest INTEGRITY and transformative IMPACT.

  2. TRAIN the next generation of fearless explorers and thought leaders

  3. ENGAGE, SUPPORT, and ENRICH our broader community through outreach and service

Research Program

We have deep expertise in the multidisciplinary investigation of brain substrates for flexible behavioral control and reinforcement learning (RL) across analysis levels.

 

We develop and deploy tools to define: 

  1. The anatomical, functional, and computational properties of brain decision circuits

  2. Link identified biological mechanisms to computational operations within normative mathematical frameworks

  3. To ultimately understand how the circuit and computational specializations become leveraged during various behavioral demands.

In a series of papers, our past contributions and discoveries have (re)shaped computational formalizations of dopamine’s functions within RL frameworks, providing complementary advances in theoretical and mechanistic understanding of reward learning, decision making, and agentic control.

 

1) Striatal dopamine dynamics multiplex learning and motivational signals within and across trials

(Hamid et. al., 2016 Nature Neuroscience)​ -- MOST CITED EMPERICAL STUDY ON DOPAMINE IN 10 YEARS!

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2) Dissociated dopamine cell spiking in the midbrain -vs- release in the striatum

(Mohebi et. al., 2019 Nature, Hamid. 2021, CurrOpin BehavSci)

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3) Discovery of dopamine waves that facilitate reward credit learning across subexperts in the dorsal striatum

(Hamid et. al., 2021. Cell)

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Current Projects:

 

Basic Science Research

  1. Computing with #DopamineWaves

    • ​​​How do various spatiotemporal circuit mechanisms implement computations (e.g. credit assignment and circuit inference) critical for behavioral control?

  2. Timescales DA fluctuation in risk, reinforcement, and choice execution 

    • How do fluctuations in striatal DA over minutes and hours govern choice arbitration, learning from gains vs losses, and errors in valuation or execution? 

  3. Computational principles and brain mechanisms for agentic inference, planning, and reward credit learning 

    • How do we leverage cortical-basal ganglia computations and circuit specializations to plan, monitor, and learn from our actions?

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​Fundamental Research meets Real-World Applications

  1. Cortical, striatal, and DA failure modes in psychiatry, aging, and neurodegeneration #ComputationalPsychiatry

  2. Synergy between Hebbian and RL learning rules across corticostriatal and task hierarchies #BioInspired AI

  3. Reinforcement learning principles for K-12 classroom educational pedagogies #NeuroscienceofActiveLearning

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