# A Synaptic Basis for Dopamine-Driven Reinforcement Learning in Cortex

> **NIH NS K99** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2026 · $126,900

## Abstract

Project Abstract
 How the brain reinforces neural activity to drive learning is a fundamental question in neuroscience.
Dopamine is a critical neuromodulator that shapes neural circuits during reinforcement learning by modifying
synaptic connections that encode rewarding behaviors. However, the precise synaptic mechanisms by which
dopamine-dependent plasticity sculpts behaviorally relevant cortical ensembles remain poorly understood. A
major barrier to addressing this question has been the lack of experimental approaches that allow for real-time
control of reinforcement signals while simultaneously tracking their effects on synaptic activity. Overcoming this
limitation is essential for uncovering how dopamine influences synaptic connectivity and circuit function to drive
adaptive behavior.
 In this proposal, I will utilize cutting-edge in vivo imaging technology, combined with newly developed
opsins and sensors for observing and manipulating dopamine dynamics, to implement a novel brain-machine
interface (BMI) paradigm to study the role of dopamine in reinforcement learning at the level of individual
synapses. This research is structured across a K99 mentored phase and an R00 independent phase, with three
specific aims. In Aim 1, I will employ a novel optical BMI paradigm combined with two-photon calcium imaging
to characterize how dopamine-driven reinforcement learning reorganizes synaptic inputs onto behaviorally
relevant cortical ensembles. In Aim 2, I will track functional synaptic activity during reinforcement learning to
determine how dopamine directly alters synaptic activity strength and dynamics over time. Finally, in Aim 3, I will
use genetically encoded dopamine sensors and optogenetics to map the spatiotemporal release of dopamine,
and apply chemogenetic and pharmacological manipulations to assess where, when, and how dopamine drives
synaptic plasticity in vivo. These experiments will leverage numerous advanced methodologies, some of which
were developed

## Key facts

- **NIH application ID:** 11283614
- **Project number:** 1K99NS146613-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Eddy  Albarran
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NS
- **Fiscal year:** 2026
- **Award amount:** $126,900
- **Award type:** 1
- **Project period:** 2026-01-16T00:00:00 → 2027-12-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11283614

## Citation

> US National Institutes of Health, RePORTER application 11283614, A Synaptic Basis for Dopamine-Driven Reinforcement Learning in Cortex (1K99NS146613-01). Retrieved via AI Analytics 2026-07-12 from https://api.ai-analytics.org/grant/nih/11283614. Licensed CC0.

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