# CRCNS: Decision dynamics in cortico-basal ganglia-thalamic networks

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $318,458

## Abstract

The mammalian brain is particularly well suited for managing streams of (often noisy) evidence, both 
internally and externally generated, to converge to a decision. This evidence accumulation process can 
adapt to changing environments and reward opportunities, mediated by cortico-basal-ganglia-thalamic 
(CBGT) circuits that both contribute to action selection and use feedback signals to modify future 
decisions. Dysfunction in how these pathways use feedback to guide future decisions is a primary 
mechanism for many addictive behaviors (e.g., opioid addiction, obesity). Our prior work has identified 
subsystems, which we call control ensembles, within the CBGT pathways that regulate dimensions of the 
evidence accumulation process, leading to various neural states with differing receptivity to the evidence 
streams that drive decisions, encapsulated in a particular decision policy.
We propose a series of empirical and theoretical investigations that bridge across levels of analysis to 
understand the flow of information through CBGT circuits during the decision-making process. On the 
theory side we will use our models to understand the low-dimensional representational space of CBGT 
circuits throughout the decision-making process, using energy landscape models coupled with 
dimensionality reduction. Using computational models we will model decision trajectories through CBGT 
networks by applying entropy based analyses to the network behavior and building predictions of observed 
dynamics in both discrete and continuous actions (Specific Aim 1). Empirically, we will test predictions 
emerging from our network model and provide new observations to support model refinement using 
experiments in rodents (optogenetics, electrophysiology) as they perform both tasks with dynamic reward 
contingencies featuring either discrete choices or continuous motor control (Specific Aim 2). Our theoretical 
and empirical work will evolve in a mutual-development cycle, with theoretical experiments being used to 
derive novel behavioral and neural predictions and empirical experimental results being used to revise and 
update the generative model properties that lead to subsequent predictions.

## Key facts

- **NIH application ID:** 10887651
- **Project number:** 5R01DA059993-02
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Jonathan E. Rubin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $318,458
- **Award type:** 5
- **Project period:** 2023-07-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10887651

## Citation

> US National Institutes of Health, RePORTER application 10887651, CRCNS: Decision dynamics in cortico-basal ganglia-thalamic networks (5R01DA059993-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10887651. Licensed CC0.

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