# CRCNS: Online optimization for probing high-level auditory representations

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $402,478

## Abstract

Biologically important sounds, such as animal vocalization, speech, and tonal music, contain rich harmonics with
spectral energy clustered at integer multiples of the fundamental frequency. Although the exact neural coding
mechanisms for harmonic sounds remain unclear, recent experiments show that harmonic sensitivity is
widespread in the auditory cortices of the marmoset. Since cortical harmonic sensitivity spans multiple octaves,
it is derived presumably by combining subcortical inputs that typically prefer only a single frequency. We
propose to study harmonic coding in auditory cortex of the marmoset by simultaneous recording of many
individual neurons which are probed automatically by an online adaptive stimulus optimization procedure based
on explicit computational models of the underlying neural circuits. Conventional methods are incapable of fully
characterizing complex harmonic responses because of the combinatorial explosion of the stimulus space,
which is a general obstacle for sensory coding research. We propose to overcome this obstacle using an
adaptive online approach to harmonic stimulus design. We will apply two broad types of methods, one is to find
the optimal stimulus that best drive a neuron, and the other is model-based stimulus design that can effectively
identify each given model and compare competing models by finding the stimuli that best distinguish them. We
will develop: (1) automated system to characterize harmonic sensitivity of individual neurons across multiple
layers of auditory cortex using Neuropixels recording probes, (2) automated system to characterize harmonic
sensitivity in auditory cortex across multiple octaves of frequencies using two-photon imaging, and (3)
generative circuit models for efficient coding of harmonic sounds in auditory cortex. By restricting the stimuli to
harmonic sounds, which are complex enough but still tractable, we believe our methods are more likely to
achieve significant success. We have obtained promising preliminary results in several successful online
neurophysiological experiments using single-unit recording. Extending our online methods to Neuropixels
recording and two-photon imaging is a logical next step and may potentially benefit many researchers working
on related problems. We expect to obtain full stimulus-response landscapes of cortical neurons together with
inferred circuit models that may explain how exactly a higher-level cortical representation of harmonics may
arise from simpler input components, and all these representations will be examined in the context of efficient
coding of natural sounds.

## Key facts

- **NIH application ID:** 10891742
- **Project number:** 5R01DC021609-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** XIAOQIN WANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $402,478
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10891742

## Citation

> US National Institutes of Health, RePORTER application 10891742, CRCNS: Online optimization for probing high-level auditory representations (5R01DC021609-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10891742. Licensed CC0.

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