# Predictive Computational Models of Olfactory Networks

> **NIH NIH U19** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $386,114

## Abstract

SUMMARY (Project 5)
The nature of perceptual objects and of the neuronal mechanisms leading to their representation in the brain is
one of the fundamental questions in neuroscience. Do representations of perceptual objects populate spaces
of low dimensionality or do they mirror the complexity of the stimulus space? What features of the stimulus are
represented by the dimensions of the perceptual space? How can objects represented in the brain retain
invariance with respect to variations in stimulus features, timing, and background?
 Despite substantial progress in our understanding of the molecular basis of the sense of smell, for the
olfactory system, these questions remain unanswered. In the eye, for example, the responses of the three
types of cone photoreceptors correspond to the three dimensions sensed by human color vision.
Understanding the low dimensional nature of color space was fundamental to our understanding of color
vision. In the olfactory system, a similar conceptual understanding is missing.
 This project is a part of synergistic effort to understand the nature of olfactory coding. Based on
experimental datasets collected by other projects of the same U19 program as well as publically available
datasets, we will study the structure of the spaces of olfactory stimuli, responses of olfactory neurons, and
perceptual qualities, build a neural network model that establishes connections between spaces, and resolve
conceptual questions to make this network biologically realistic. Using state-of-the-art machine learning
approaches, we will generate a predictive computational model of the olfactory system as a deliverable.
 Our goal is to develop, implement in a computational model, and test at least two theoretical ideas about
the nature of olfactory code. First, we will test the hypothesis that olfactory spaces contain substantially fewer
dimensions than the number of types of odorant receptors (OR). Our preliminary data indicates that the
number of principal dimensions may be as low at 10, compared to ~103 of OR types. We will define these
dimensions mathematically and relate them to the molecular properties of odorants. Second, we will test the
primacy coding hypothesis, according to which identities of a small cohort of the most sensitive olfactory
receptor types represent odorant identity in a concentration-invariant manner. Such representations render
odor objects robust to noise. Our computational/theoretical studies will be carried out in close collaboration with
the experimental groups. Our project includes three Specific Aims: Aim 1: To build predictive computational
models for spaces of olfactory stimuli, responses, and percepts; Aim 2: To develop a predictive network model
for mapping between olfactory spaces; and Aim 3: To build biologically realistic models of olfactory networks.
Since representation of sensory objects is a fundamental problem in neuroscience, mathematical principles
uncovered by our studies will elucidate ...

## Key facts

- **NIH application ID:** 10200170
- **Project number:** 5U19NS112953-03
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** ALEXEI KOULAKOV
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $386,114
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200170

## Citation

> US National Institutes of Health, RePORTER application 10200170, Predictive Computational Models of Olfactory Networks (5U19NS112953-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10200170. Licensed CC0.

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