# Revealing the organization and functional significance of neural timescales in auditory cortex

> **NIH NIH K99** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2021 · $90,340

## Abstract

Project Summary
People are remarkably adept at making sense of the world through sound: understanding speech in a noisy
restaurant, picking out the voice of a family member, or recognizing a familiar melody. Although we take these
abilities for granted, they reflect impressive computational feats of biological engineering that are remarkably
difficult to replicate in machine systems. The long-term goal of my research program is to develop computational
and experimental methods to reverse-engineer how the brain codes natural sounds like speech and to exploit
these advances to understand and aid in the treatment of hearing impairment. One of the central challenges of
coding natural sounds is that they are structured at many different timescales from milliseconds to seconds and
even minutes. How does the brain integrate across these diverse timescales to derive meaning from sound?
Answering this question has been challenging because there are no general-purpose methods for measuring
neural timescales in the brain. As a consequence, we know relatively little about how neural timescales are
organized in auditory cortex and how this organization enables the coding of natural sounds. To overcome these
limitations, we develop a simple experimental paradigm (the “temporal context invariance” or TCI paradigm) for
estimating the temporal integration period of any sensory response: the time window during which stimuli alter
the response. We apply the TCI method to human electrocorticography (ECoG) and animal physiology
recordings to reveal the organization of neural timescales at both the region and single-cell level (Aim I). Pilot
data from our analyses reveal that timescales are organized hierarchically, with higher-order regions showing
substantially longer integration periods. To explore the functional significance of this timescale hierarchy, we
couple TCI with computational techniques well-suited for characterizing natural sounds (Aim II). We test whether
increased integration periods enable a more noise-robust representation of speech (Aim IIA), whether regions
with longer integration periods code higher-order properties of natural sounds (Aim IIB&IIC), whether there are
dedicated integration periods for important sounds categories like speech or music (Aim IID), and whether
cortical integration periods can be explained by the duration of the features they respond to (Aim IIE). In the
process of conducting this research, I will be trained in two critical areas: (1) ECoG, which is the only method
with the spatial and temporal precision to understand how neural timescales are organized in the human brain
(2) deep neural networks (DNN) which are the only models able to perform challenging perceptual tasks at
human levels and predict neural responses in higher-order cortical regions. After completing this training, I will
have a unique set of experimental (fMRI, ECoG, psychophysics) and computational skills (data-driven statistical
modeling and hypothesis...

## Key facts

- **NIH application ID:** 10169404
- **Project number:** 5K99DC018051-02
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Samuel V Norman-Haignere
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $90,340
- **Award type:** 5
- **Project period:** 2020-06-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10169404

## Citation

> US National Institutes of Health, RePORTER application 10169404, Revealing the organization and functional significance of neural timescales in auditory cortex (5K99DC018051-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10169404. Licensed CC0.

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