# Data-driven analysis for neuronal dynamic modeling

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $330,552

## Abstract

Project Summary / Abstract
Our main goal is to unravel communication dynamics in the brain, as they relate to various sensory-motor
actions and to the learning process. The sensory-motor system operates through the concerted interaction of
multiple closed-loops feedback systems. While some broad level knowledge is available about single neuron
properties and general high-level operations, we lack understanding of functional aspects of neural dynamics, of
inter-neuronal interactions and of the modular interaction and integration of brain regions contributing to motor
activity. Two photon calcium imaging has revolutionized experimental capabilities to measure large-scale
neuronal activity, but poses a significant challenge in terms of massive dynamical data analysis.
We intend to confront these challenges face-on, to significantly boost the quality and relevance of experimental
data collected during the process of animal learning and execution of motor functions. Our goal is to build an
end-to-end modular platform to organize automatically (in a data agnostic way) the dynamical observation space
into dynamic scenarios corresponding to contextual groups of neuronal dynamics and to specific motor activity
in different related trials. Our plan follows a path from low-level processing of raw calcium imaging data through
mid-level organization of extracted neuronal time-traces and finally to high-level inference and prediction of
behavior.
We aim to extend prior geometric dynamics analysis methods for nonlinear empirical modeling to the complexity
of the large-scale neuronal data. Our methodology leads to the determination of low-dimensional intrinsic
dynamical sub-processes that provides a coherent explanation of the observed data, and to testable
experimental predictions. Unlike conventional neuronal data processing postulating a-priori specific structural
models, we rely only on general data-agnostic coherence assumptions. These settings remove bias due to a-
priori modeling and enable developing tools that are independent of the acquisition modality, simplifying data
fusion (such as neuronal and behavioral observations).
Our initial experimental setup is two photon calcium imaging measurements of a head-fixed mouse, performing
a motor reach task in multiple repetitive trials. The brain imaging data is synchronized with acquired high-
resolution behavioral video. As we show in this proposal, we already have preliminary results demonstrating the
power of our empirical analytics methods.

## Key facts

- **NIH application ID:** 10009336
- **Project number:** 5R01EB026936-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Gal Mishne
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,552
- **Award type:** 5
- **Project period:** 2019-09-06 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10009336

## Citation

> US National Institutes of Health, RePORTER application 10009336, Data-driven analysis for neuronal dynamic modeling (5R01EB026936-03). Retrieved via AI Analytics 2026-06-23 from https://api.ai-analytics.org/grant/nih/10009336. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*