# Mechanisms for invariance in auditory cortex: Investigations with marmoset electrophysiology

> **NIH NIH F32** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $64,926

## Abstract

Project Summary
Listening in noise is a core problem in everyday hearing. Sound sources of interest routinely occur amid
irrelevant distractors, as when you talk with someone in a bustling coffee shop. This background “noise”
distorts the pattern of spikes in the auditory nerve, often to a profound degree. Thus, to recognize sources of
interest, the auditory system must somehow separate or suppress the effects of the background. Typical
human hearing is remarkably noise-robust, but listeners with age-related hearing loss or other forms of
impaired hearing struggle in noisy environments – and are not much helped by contemporary hearing aids.
Previous work on the neural basis of noise robustness has typically employed simple, synthetic noise sources,
which lack the structure present in real-world sounds, and this work has focused on subcortical regions or on
primary auditory cortex. Reasoning that real-world conditions might necessitate more complicated solutions, in
the applicant's doctoral work, he considered everyday sources of noise, and leveraged the large-scale
coverage afforded by fMRI to examine noise robustness throughout human auditory cortex. Real-world
“background noise” was operationalized as a natural sound with statistical properties that are stable over time
(i.e., are stationary), conveying little new information about the world (e.g., swamp insects, an air conditioner,
rain on pavement). The applicant measured fMRI responses in human listeners to a broad set of natural
sounds presented in quiet, as well as embedded in the real-world background noises. Primary auditory cortical
responses were substantially altered by the background, but non-primary responses were substantially more
robust. This effect was not seen for simple synthetic backgrounds as had been used in previous work,
suggesting that becoming robust to real-world background noises require different mechanisms.
The applicant's thesis work demonstrates where noise invariance arises, but understanding how will require
data with finer spatial and temporal resolution, and thus the proposed postdoctoral research will consist of
training in single-unit electrophysiology using marmosets. Aim 1A builds on previous work examining single-
unit noise robustness in artificial conditions, extending such work to real-world noise. Aim 1B leverages texture
models to probe what aspects of real-world backgrounds disrupt the encoding of foregrounds. Aim 2A deploys
linear reconstruction techniques to probe population representations. Aim 2B involves optimizing deep neural
networks for noise invariance tasks, and using them as an encoding model to predict single-unit responses.
Furthermore, such networks will be deployed as nonlinear decoding algorithms, reconstructing stimuli from
neuronal populations. Throughout all aims, the work will characterize neuronal responses in non-primary
areas, and in particular in parabelt, which is understudied in primates. The proposed work may enable
improv...

## Key facts

- **NIH application ID:** 10104430
- **Project number:** 5F32DC017628-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Alexander James Eaton Kell
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $64,926
- **Award type:** 5
- **Project period:** 2019-03-19 → 2022-03-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10104430

## Citation

> US National Institutes of Health, RePORTER application 10104430, Mechanisms for invariance in auditory cortex: Investigations with marmoset electrophysiology (5F32DC017628-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10104430. Licensed CC0.

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