# Machine Learning for Analyzing State Dependent Neuronal Network Dynamics

> **NIH NIH F31** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2024 · $41,317

## Abstract

Calcium imaging allows recording from 100s of neurons in a single wide field of view, giving rise
to extremely high dimensionality data. Current analysis standards employ descriptive statistics
that summarize neuronal responses into single quantitative metrics, discounting the temporal
dynamics of individual cells and local networks. In contrast, machine learning, especially
dimensionality reduction models, provide more nuanced analysis that considers the temporal
patterns and groupings among cells. While previous work has attempted to reduce the neuronal
activity to very low dimensional manifolds, these methods result in outputs that are difficult to
understand. In this work, we adapt Non-Negative Matrix Factorization (NMF), an easily
interpretable dimensionality reduction method to analyze shifts in neuronal network dynamics
that arise as a function of different experimental contexts. We will apply our framework to study
the neuronal network dynamics of two different contexts: 1) the primary somatosensory cortex
(S1) under increasing concentrations of anesthesia, and 2) the hippocampus during optogenetic
stimulation of memory-encoding ensembles of neurons. We have successfully adapted and
characterized a series of dimensionality reduction methods and have demonstrated NMF is a
superior method to extract underlying structure from calcium recordings. Initial analyses have
extracted ordered, low-dimensional, internal structure not detectable with traditional statistics.
This research will be conducted at Boston University, taking advantage of the numerous
multidisciplinary research centers (Center for Systems Neuroscience, Neurophotonics Center,
Rafik B. Hariri Institute for Computing and Computational Science & Engineering). These
institutes, consisting of highly diverse and renowned groups of faculty, create a highly
collaborative environment for interdisciplinary research, allowing scientists to pursue interesting
questions not directly in their expertise. Further, a combination of academic training,
development of technical skills, analytical problem solving, scientific communication,
professional development, and consistent mentorship will ensure the project has the highest
potential to succeed possible.

## Key facts

- **NIH application ID:** 10919177
- **Project number:** 5F31MH133306-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Daniel David Carbonero
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $41,317
- **Award type:** 5
- **Project period:** 2023-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10919177

## Citation

> US National Institutes of Health, RePORTER application 10919177, Machine Learning for Analyzing State Dependent Neuronal Network Dynamics (5F31MH133306-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10919177. Licensed CC0.

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