JEONG @ NIH - Research

My research aims to understand how the brain’s neural circuits and molecular mechanisms enable reinforcement learning, and how addictive substances and psychiatric drugs disrupt these processes to drive maladaptive reinforcement and circuit dysfunction.

By integrating in-vivo optical imaging, protein engineering, and computational analysis of large-scale neural recordings, I investigate dopamine neuromodulation, GPCR-mediated signaling, and neural network dynamics to delineate the cellular and circuit mechanisms of reinforcement learning, action control, and behavioral flexibility. Key questions driving my research include:

(1) How do dopamine activity and intracellular messengers like cAMP dynamically regulate synaptic and circuit plasticity during learning?

(2) How do neural circuit dynamics encode, retain, and adaptively refine learned behaviors over time in response to changing behavioral contexts?

(3) How do perturbations in these signaling and circuit mechanisms contribute to neuropsychiatric disorders?

Through this interdisciplinary approach, my work seeks to bridge molecular mechanisms and systems-level neural function to advance therapeutic discovery for drug addiction and neuropsychiatric disorders.

Neural Computation in the Cortex

The brains of humans and many other animals possess an extraordinary ability to flexibly organize complex and dynamic movement sequences through reinforcement learning. How do brain circuits compute behavioral information and regulate motor output during this process? My research explores the cortical circuits and neural dynamics involved in organizing complex behaviors. Using in-vivo optical imaging and computational techniques, I focus on decoding the neural network dynamics of cortical microcircuits that control action sequence, reinforce motor learning, and enable flexible motor behaviors. In my most recent project (link), I use large-scale single-cell calcium imaging and opto/chemogenetic manipulations in freely behaving mice to decode the intricate dynamics of cortical interneuron networks that actively regulate movement sequences. This work seeks to define the circuit-level neural computations through which the neocortex implements reinforcement learning and flexibly adjusts action policies, providing a framework for understanding adaptive decision-making and context-dependent behavior.

Reward Processing Circuits in the Basal Ganglia

Neural substrates involved in reward processing and reinforcement learning (RL) are essential for biological organisms to acquire new skills, make decisions, and adapt to changing environments. I investigate how neural circuits in the basal ganglia encode reinforcement and error signals that drive learning and behavioral adaptation. Additionally, my research explores how pharmacological interventions and addictive drugs disrupt these processes.

Using in-vivo optical methods and animal behavior models, I examine how dopamine neuromodulation is differentially involved in reward-based learning and drug responses across striatal subregions (link). I also aim to investigate the mechanisms of intracellular signaling and neural circuit dysfunction associated with chronic exposure to psychoactive drugs, including addictive substances, selective receptor agonists, alcohol, and pain management drugs. In the long term, these approaches will enhance our understanding of the neural mechanisms underlying reward-based learning and offer new insights into pharmacological treatments for neurological disorders associated with basal ganglia circuits.