# Mapping of spatiotemporal code features to neural and perceptual spaces

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $432,624

## Abstract

Project Summary
Two of the most fundamental questions of sensory neuroscience are: 1) how is stimulus information
represented by the activity of populations of neurons at different levels of information processing? and 2) what
features of this activity are read at the next levels of neural processing to guide behavior? The first question
has been the subject of a large body of work across different sensory stimuli. To answer the second question,
one needs to establish a causal link between neuronal activity and behavior. In many systems sensory
information is represented by complex spatiotemporal patterns of neuronal activity. Novel recording and
stimulating technology will soon allow the precise temporal control of hundreds and thousands of individual
neurons, however, conceptual approaches of finding relevance of different spatiotemporal features of neural
code still lag behind.
To develop a new approach we chose the mammalian olfaction as a model system, because odor stimuli evoke
complex patterns of glomerular activity with spatial and temporal scales fully compatible with existing imaging
and pattern stimulation technologies. In addition, the accessibility of the cells in the next processing level, the
mitral/tufted cells which get input from olfactory glomeruli and transmit the signal to higher brain areas, allows
a systematic study of encoding different features of neural activity with known behavioral relevance.
We propose a novel approach to map spatiotemporal code features to neural and perceptual spaces. First, we
substitute sensory-driven neural activation by artificial and fully parametrized optogenetic pattern stimulation.
By varying the parameters of such stimulation and recording the behavioral outcomes of the stimulation, we
will build a detailed empirically-validated mathematical model of the relevance of different features of neural
activity. Then we will test this model for natural odor stimuli, and explore how these features are processed
and encoded by the next level of processing.
Successful execution of the project will produce the first (to our knowledge) causally validated model for
behavioral relevance of a distributed neural code. It will shine light on long standing questions in olfactory
processing, approaching the olfactory code from the perspective of its behavioral relevance. The proposed
approach can be further applied to different neural systems using multi-neuronal recording and stimulation
techniques.

## Key facts

- **NIH application ID:** 10437652
- **Project number:** 5R01NS109961-05
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Stefano VT Panzeri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $432,624
- **Award type:** 5
- **Project period:** 2018-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10437652

## Citation

> US National Institutes of Health, RePORTER application 10437652, Mapping of spatiotemporal code features to neural and perceptual spaces (5R01NS109961-05). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10437652. Licensed CC0.

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