# Neural basis of behavioral timescale coordination

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2024 · $521,538

## Abstract

Behavior is organized across multiple spatial and temporal scales, ranging from 
sub-second motor commands over multi-second movement plans to long term foraging patterns. 
Currently it is unclear how the brain solves this coordination of multiple intertwined 
temporal demands. While classical neuroscience experiments typically look at or engage a 
fixed temporal scale or horizon, ethological studies have long focused on the analysis of 
naturalistic behavior across freely elicited temporal scales. Here we will use novel developed deep 
learning pose estimation approaches to study the behavior and associated single unit physiology of 
foraging behavior in freely moving rhesus macaques. First we will establish that timescales of 
macaque pose behavior encode cognitive variables such as reward expectation. Second we will 
establish the link between neural and behavioral timescale coordinating in the decision to 
action axis of the medial prefrontal wall leading to the anterior cingulate cortex by 
recording multi-region wireless electrophysiology in freely moving rhesus macaques. Third, 
 using embedding and connectivity analysis we will uncover the mechanisms for inter and intra areal 
timescale coordination to understand how the brain balances temporal scale demands.

## Key facts

- **NIH application ID:** 10735877
- **Project number:** 5R01MH128177-03
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Jan Zimmermann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $521,538
- **Award type:** 5
- **Project period:** 2021-12-15 → 2026-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10735877

## Citation

> US National Institutes of Health, RePORTER application 10735877, Neural basis of behavioral timescale coordination (5R01MH128177-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10735877. Licensed CC0.

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