# Neural mechanisms underlying vocalization perception in realistic listening conditions

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $411,378

## Abstract

ABSTRACT
A fundamental question in audition is how complex sounds are accurately recognized despite wide variations in
listening conditions. Accurate and robust recognition is especially important for behaviorally critical sounds
such as speech or animal vocalizations (calls). Large individual differences in the production of speech or calls,
and environmental distortions due to reverberations and noise, result in considerable variability in the acoustics
of calls when they arrive at the ear. A critical function of the auditory system is to group these diverse signals
into functional categories, such as a call type in animal communication, or words in human speech. In this
proposal, we will begin to elucidate the mechanisms by which invariant categorization of calls is accomplished
in auditory cortex. In Aim 1, we will first develop and validate a model for how neural circuits might achieve call
categorization while remaining invariant to production variability. This model makes specific predictions about
the tuning properties of single neurons and neural populations in auditory cortex. We will test these predictions
experimentally using in-vivo recordings in awake Guinea pigs. Preliminary data suggests that neurons in
superficial cortical layers (L2/3) of primary auditory cortex (A1) encode ‘mid-level’ features that underlie
production-invariant call categorization. In Aim 2, we will test the hypothesis that both production-invariant
selectivity to specific call types, as well as a high degree of invariance to listening conditions co-emerge in A1
L2/3. We will analyze the responses of single neurons as well as neural populations in thalamus, A1 layer 4
(L4), and A1 L2/3 to call stimuli presented in ideal and systematically degraded listening conditions. Preliminary
data suggest that A1 L2/3 neurons, but not thalamic neurons, are able to maintain their feature selectivity in a
wide range of listening conditions. Finally, in Aim 3, we will test the hypothesis that cortical inhibition is crucial
for generating invariance to listening conditions, but not call selectivity. Specifically, we will use optogenetic
approaches with novel viral vectors to characterize the effects of decreased inhibition on selectivity and
invariance in A1 single neurons and the A1 population. Together, the theoretical model, and behavioral,
electrophysiological, and optogenetic data will give rise to a general computational principle by which the
auditory cortex extracts useful features from noisy inputs. Insights gained from these experiments will provide a
novel framework for understanding auditory processing in normal circuits, as well as during circuit dysfunction
in communication disorders such as autism, dyslexia and specific language impairment.

## Key facts

- **NIH application ID:** 10434867
- **Project number:** 5R01DC017141-05
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Srivatsun Sadagopan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $411,378
- **Award type:** 5
- **Project period:** 2018-07-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10434867

## Citation

> US National Institutes of Health, RePORTER application 10434867, Neural mechanisms underlying vocalization perception in realistic listening conditions (5R01DC017141-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10434867. Licensed CC0.

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