# CRCNS: Identifying principles of auditory cortical organization with machine learning

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2023 · $354,609

## Abstract

The human auditory system transforms incoming acoustic information into distinct auditory “objects” that
can be interpreted, localized, and integrated with information from the other senses. A human listener can
resolve, for example, a monophonic musical recording into piano and guitar, or parse speech audio into a
sequence of words. We do not currently understand how this is achieved, nor how transformations in
sound processing across cortical regions contribute, especially beyond primary auditory cortex.
On the other hand, we now have available another, more easily investigated system that—as of this last
decade—solves such problems at human performance levels: the artificial neural network (ANN).
Although crude as biophysical models, ANNs strongly resemble biological neural networks in terms of
computation and representation. We propose to compare single-unit electrophysiology in macaque
auditory cortex with state-of-the-art ANNs trained to solve ecologically relevant auditory tasks. This
combination allows us to track how transformations in perceptual representations are distributed and
instantiated in the brain; to experiment with multiple ANN architectures and tasks to test hypotheses about
why the representations take the forms we observe; and to refine iteratively our stimulus protocols and
models. Our objectives are (1) to evaluate these ANNs as encoding models for neurons in macaque
auditory cortex, recorded during auditory discrimination tasks; (2) to use the internal structure of ANNs to
generate and test novel hypotheses about the topographical and hierarchical organization of non-primary
auditory cortex; and (3) to demonstrate “stimulus- based control” of neurons throughout nonprimary
auditory cortex: optimizing stimuli via the ANN and then playing to the animals in closed-loop
neurophysiology experiments.

## Key facts

- **NIH application ID:** 10830506
- **Project number:** 1R01DC021600-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Joseph Gerard Makin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $354,609
- **Award type:** 1
- **Project period:** 2023-07-12 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10830506

## Citation

> US National Institutes of Health, RePORTER application 10830506, CRCNS: Identifying principles of auditory cortical organization with machine learning (1R01DC021600-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10830506. Licensed CC0.

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