# CRCNS: US-Israeli Research Proposal: Deciphering reorganization of multi-regional activity following category learning

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $202,834

## Abstract

PROJECT SUMMARY (See instructions):
Learning is a fundamental component of neural systems. It is essential for gaining new knowledge or skills, 
and impaired learning faculties can have major deleterious effects. Although various forms of associative 
and perceptual learning have been studied our current understanding of how neural circuits learn remains 
incomplete. Humans and animals leverage category learning––grouping stimuli together based on shared 
and often higher order features––to deal with the world’s dazzling complexity. Yet, the neural mechanisms 
underlying category learning are not known. In this proposal, we focus on auditory category learning 
leveraging the rich nature and dynamic structure of natural and synthetic soundscapes. Auditory category 
learning is ubiquitous across the animal kingdom, with vivid examples from invertebrates to humans, and is 
intimately related to the transformation of sound to percepts. However, mechanistic studies of category 
learning in audition are not yet mature. In mice, where powerful experimental toolkits exist and new tools 
are continuously developed, studies of auditory category learning are scarce. Here we will study the multi-regional basis of reshaping of population activity following learning. We will analyze a unique dataset of 
simultaneous population recordings across brain areas in mice learning an auditory categorization task. We 
will apply novel computational methods to study how cortical auditory circuits process sounds and how this 
processing changes following learning. We will explore multiple types of trained categorization as well as 
responses to categories of natural sounds. We will characterize changes both at the level of single neurons 
and at the level of neuronal populations. Through simultaneous recordings across brain areas we will 
delineate changes both at the level of individual brain areas and at the level of interactions between brain 
areas. Finally, we will use perturbations to improve our causal understanding of the circuitry. These 
experiments and analyses will allow us to explore specific hypotheses regarding the plasticity and circuit 
computations of cortical circuits in the mammalian brain, and how these support category learning.

## Key facts

- **NIH application ID:** 10874493
- **Project number:** 5R01DC020874-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Shaul Druckmann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $202,834
- **Award type:** 5
- **Project period:** 2022-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874493

## Citation

> US National Institutes of Health, RePORTER application 10874493, CRCNS: US-Israeli Research Proposal: Deciphering reorganization of multi-regional activity following category learning (5R01DC020874-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10874493. Licensed CC0.

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