# Coarse-graining approaches to networks, learning, and behavior

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2020 · $353,534

## Abstract

Project Summary
The theory hub put forward in this proposal will work to translate successful and powerful approaches to
describing emergent collective behavior in physical systems so they can be applied to the brain. Working
closely together, the three theorists will develop methods for finding and quantifying the relevant modes of
population activity in the brain, both in instantaneous snapshots of activity and activity as it evolves in time.
Methods will be tested in a wide range of neural systems at different processing stages and scales: from
salamanders to rodents to humans, from the retina to the cortex, from tens to thousands of cells. The approach
will be validated by checking that the neural code can be read out with high fidelity even after being
compressed into a much smaller subspace. The project will produce data analysis code that will be made
available for neuroscience researchers to use on their own data, in addition to the results of the analyses of the
particular systems studied.
The neural code is inherently collective; while single neurons execute sophisticated computations, hundreds to
thousands of neurons are utilized to sense the environment and drive behavior in even the simplest organisms.
Although the past hundred years have yielded substantial progress in neuroscience, only recently have
researchers had the capacity to record from complete neural populations - that is, to view the collective
behavior of a functioning neural network. With these rapid experimental advances, there is an urgent need for
complementary theoretical and computational approaches to guide the exploration of emergent behavior in
large groups of neurons, allowing one to turn `big data' into `big ideas'. This proposal outlines a path towards a
new theoretical framework for finding and quantitatively analyzing collective phenomena in the brain that
underlie sensory coding, the representation of space, prediction, and ultimately drive behavior. The project
draws heavily on the success of so-called renormalization group approaches in theoretical physics that
revolutionized the understanding of collective phenomena in physical systems, and sculpted much of the
progress in statistical physics in the second half of the twentieth century. The methods explored in this
proposal generalize such techniques so they can be applied to a much wider range of problems.
The methods developed by this theory hub based on the renormalization group will be applicable to a wide
range of neural data since they are explicitly designed to generalize techniques from theoretical physics to a
much broader setting. Indeed, a larger goal of the approach is to search for universality in collective behavior in
the neural code. The techniques proposed are relatively straightforward to execute and will provide a
fundamental methodology for interrogating high-dimensional data in fields as diverse as behavioral
neuroscience and biophysics. The new techniques will also be taught as par...

## Key facts

- **NIH application ID:** 10002224
- **Project number:** 5R01EB026943-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** WILLIAM BIALEK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $353,534
- **Award type:** 5
- **Project period:** 2018-09-20 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002224

## Citation

> US National Institutes of Health, RePORTER application 10002224, Coarse-graining approaches to networks, learning, and behavior (5R01EB026943-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10002224. Licensed CC0.

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