# Combined Topological and Machine Learning Tools for Neuroscience

> **NIH NIH RF1** · COLD SPRING HARBOR LABORATORY · 2020 · $2,048,230

## Abstract

Two major recent advances have raised the possibility of fundamental breakthroughs in both
basic and clinical neuroscience: the development of new tools to probe the nervous system with
single-cell resolution as well as brain-wide scope, and breakthroughs in machine learning
methods for handling complex data. Yet there remain crucial barriers to progress: while data
acquisition tools are now broadly within the grasp of neuroscience researchers, the same cannot
be said about data analytical tools that can tackle the complexities of the new data sets being
gathered. In addition, the highly training-data dependent, black-box Artificial Neural Network
(ANN) methods that have shown rapid growth in the technological domain, are not well-suited to
scientific data analysis, where transparency and understanding is more important than black-box
performance measures. This proposal brings together a cross-disciplinary team of leading
neuroscience and computer science researchers to develop and deploy a critical set of data
analytical tools for the neuroscience community. The tools will be useful for data already gathered
in major group efforts in the US Brain Initiative, and also for new data sets being acquired using
the tools developed in the Initiative.
 Extraction of the projection morphologies of individual neurons, and the classification and
analysis of neuronal cell types is a central goal of the Brain Initiative. Because data from various
sources are often analyzed with custom algorithms, scaling up existing approaches for use across
large datasets and multiple data types has been a challenge. Instead researchers need a
comprehensive, flexible mathematical framework that can be applied to a wide variety of data,
including both static and dynamic measures. We propose to achieve this goal by combining
Topological Data Analysis (TDA) methods with Deep Net based machine learning methods. Such
a combined approach retains the flexibility of data-driven ANN methods while at the same time
brings in conceptually well-grounded methods from TDA that are still able to address the
complexities of brain-wide data sets with single-cell resolution. Aim 1 of the proposal will use
these methods to automate tasks in neuroanatomy previously requiring intensive human expert
effort. Aim 2 will apply the methods to single cell omics data sets. Aim 3 will deploy the tools
developed to the Brain Initiative Cell Census Network and the neuroscience community.

## Key facts

- **NIH application ID:** 10123310
- **Project number:** 1RF1MH125317-01
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Michael Hawrylycz
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,048,230
- **Award type:** 1
- **Project period:** 2020-09-14 → 2024-09-13

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10123310

## Citation

> US National Institutes of Health, RePORTER application 10123310, Combined Topological and Machine Learning Tools for Neuroscience (1RF1MH125317-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10123310. Licensed CC0.

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