# CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional space

> **NIH NIH R01** · COLD SPRING HARBOR LABORATORY · 2020 · $446,764

## Abstract

The first major goal of this work is to learn how certain brain regions (olfactory system, 
hippocampus, and cerebellum) learn very complex stimuli that employ a combinatorial code to 
identify stimuli as points in a high-dimensional space. For example, the simple fruit fly 
olfactory system uses the firing rates of 50 different types of odorant receptors to 
identify each odor by placing it at a point in a SO-dimensional space. Although the fly olfactory 
system is well understood, less is known about analogous regions in vertebrate brains, and our 
goal is to begin to learn about these other regions. The first step is to start with the mouse
olfactory system that is similar to the fly in some ways but has complexities that are 
absent in insects. These complexities include an enhanced ability to handle noise in the 
odor and to learn over time to discriminate between very similar odors (e.g., two types of 
red wines). Preliminary evidence shows that it should be possible to learn the role of 
these complexities in vertebrate olfaction. The research design involves studying the 
anatomy and recording the firing rates of different types of neurons at different levels of the 
mouse olfactory system and in applying computational methods and algorithms that have 
proved successful in earlier work to describe these complexities.
The second major goal is to use insights into how these brain regions operate to improve the 
function of computer algorithms. For a long time, a dream of many neuroscientists and computer 
scientists has been to understand how the brain works well enough that we could translate insights 
from the brain to improve machine computation. Indeed, experience has shown that the brain 
 has evolved novel variations of information processing algorithms used by computer 
scientists to solve general computational problems. With sufficient insight into algorithms 
used by the brain, these insights may provide unexpected ways to improve the function computer 
science algorithms. Further, understanding the circuit mechanisms involved
in olfactory processing can help illuminate the basis of a variety of smell disorders, and may 
in the future lead to the construction of artificial smelling devices.

## Key facts

- **NIH application ID:** 9994975
- **Project number:** 5R01DC017695-04
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Saket Navlakha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $446,764
- **Award type:** 5
- **Project period:** 2019-11-21 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994975

## Citation

> US National Institutes of Health, RePORTER application 9994975, CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional space (5R01DC017695-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9994975. Licensed CC0.

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