# Information Processing in Auditory Cortex

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $635,888

## Abstract

The long-term objective of this research is to understand fundamental neural coding mechanisms and neural
circuits in auditory cortex that subserve cortical representations of biologically important sounds. We will use
the common marmoset (Callithrix jacchus) as our experimental model to investigate these questions.
Marmosets have a hearing range similar to that of humans and are an ideal model system for studying audition
and deficits and diseases in hearing. With the recent progress in creating transgenic marmosets, this model
system is poised to become a major non-human primate model for hearing research. Our laboratory has
pioneered major neural recording techniques in awake and behaving marmosets. In this application, we will
focus on elucidating neural coding mechanisms underlying spatial and harmonic processing in non-primary
auditory cortex. Aim 1 will characterize functional properties of parabelt areas of auditory cortex. Aim 2 will
study cortical organization of spatial information. Aim 3 will study topographic organization of harmonic
processing in marmoset auditory cortex. Findings from the proposed study will shed lights on neural
mechanisms responsible for hearing and have implications for understanding how the auditory system
operates in normal or diseased conditions.

## Key facts

- **NIH application ID:** 10111501
- **Project number:** 5R01DC003180-25
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** XIAOQIN WANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $635,888
- **Award type:** 5
- **Project period:** 1997-01-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10111501

## Citation

> US National Institutes of Health, RePORTER application 10111501, Information Processing in Auditory Cortex (5R01DC003180-25). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10111501. Licensed CC0.

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