# Striatal Plasticity in Habit Formation as a Platform to Deconstruct Adaptive Learning

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $1,038,459

## Abstract

ABSTRACT
 A distinguishing feature of the brain is that its circuitry isn’t computationally static, it adapts to
experience. Understanding the circuit mechanisms for adaptive behavior carries two-fold potential benefits -
revealing the brain’s learning rules and identifying key behaviorally significant functional “nodes”. These nodes
suggest potent sites to target for therapy development and may also be instructive to suggest more basic
circuit principles underlying behavior.
Using striatal circuitry and habit learning as a model system, we recently uncovered a set of paradigm-
challenging findings in a striatum-dependent habit learning task. In particular, we discovered a new circuit-level
signature, termed dviLP (direct vs indirect Latency Plasticity), which distinguishes striatal slices prepared from
habitual vs goal-directed animals. The features of dviLP shift long-held attention on rate differences between
the two principle projection neuron types, those to the direct and indirect pathways, to consider that
behaviorally adaptive signals may be generated by plasticity of their relative timing to fire. Moreover, the origin
of this plasticity appears to involve striatal fast-spiking interneurons, a highly non-canonical site for the
expression of long-lasting plasticity. Beginning with this highly novel foundation, here we propose to generate a
robust predictive computational model for striatal-dependent learning mechanisms by joining multiple
disciplines and multiple levels of analysis through an iterative process of circuit modeling and experimentation.
In Aim 1, we will comprehensively map functional changes in synaptic and cellular activity that define the
behavioral transition from goal-directed to habitual in an operant lever press task. We will use a layered suite of
molecular genetic tools to assign coordinates that specify inputs, outputs, compartments (striosome/matrix)
and regions (medial, dorsal). In Aim 2, we will measure the activity of genetically specified components of the
striatum in behaving mice, identifying the dynamic changes that correlate with and cause the shift from goal-
directed to habitual behavior. Our team offers multidisciplinary strengths. Dr. Calakos and Yin have expertise in
habit behavior, plasticity mechanisms and in vivo circuit dynamics; ideal for spearheading this effort. The
success and impact of this effort will be amplified by tightly incorporating Dr. Brunel’s expertise in
computationally modeling brain learning mechanisms and Dr. Tadross’s novel pharmacogenetic reagents that
are ideally positioned to test causality of synaptic plasticity events, offering the unique opportunity to
manipulate a specific synaptic receptor in a genetically defined cell type. Ultimately, we expect that the
knowledge gained through this highly collaborative proposal will provide a foundational resource to accelerate
understanding of striatal learning rules for adaptive behavior.

## Key facts

- **NIH application ID:** 9952429
- **Project number:** 5R01NS110059-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** NICOLE CALAKOS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,038,459
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9952429

## Citation

> US National Institutes of Health, RePORTER application 9952429, Striatal Plasticity in Habit Formation as a Platform to Deconstruct Adaptive Learning (5R01NS110059-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9952429. Licensed CC0.

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