# To identify mechanisms of predictive processing across the distributed thalamocortical circuit

> **NIH NIH K99** · NEW YORK UNIVERSITY · 2024 · $133,110

## Abstract

Project Summary
Many of the sounds that animals hear are created by their own actions and being able to correctly differentiate
these sounds is critical to a range of behaviors. An influential idea is that the brain uses sensory-motor predictions
to anticipate sounds generated by movement, and identifying the circuit mechanisms that learn and implement
these predictions is critical to our understanding of cortical function in health and disease. Since predictive
computations involve the interaction of sensory and non-sensory signals, identifying underlying circuit
mechanisms will require understanding how distributed but interconnected brain regions work together. While
the thalamus is often perceived as a simple conduit of sensory information, the second-order thalamus is tightly
linked with both the sensory and motor cortex, positioning it to play a key role in integrating sensory and non-
sensory information. This proposal will test the hypothesis that the auditory second-order thalamus shapes
predictive processing throughout the auditory cortex. First, I will use a transgenic mouse line that specifically
labels second-order thalamic neurons to map the precise functional connections of the second-order auditory
thalamus (Aim 1, K99). Next, I will develop an acoustic augmented reality home cage environment where mice
can rapidly learn multiple predictive behaviors. I will perform wireless recordings while freely moving mice make
multiple sound-generating movements to determine the sensory, movement, and prediction information encoded
in the second-order auditory thalamus (Aim 2, K99). Finally, I will perform simultaneous multi-area recordings
and targeted neural interventions in the thalamus and cortex of behaving mice to determine how predictive
computations are carried out across the thalamocortical circuit (Aim 3, R00). With the guidance of my mentorship
team, I have developed a training plan at New York University that will provide me the technological skills needed
to complete these aims and make important discoveries about how distributed circuits integrate sensory and
non-sensory information during predictive processing. The proposed training plan will also provide me with the
conceptual framework and professional skills to achieve my long-term career goal: to investigate how distributed
circuits work together mechanistically to enable context-dependent auditory processing in health and disease as
an independent scientist.

## Key facts

- **NIH application ID:** 10888988
- **Project number:** 5K99DC020770-02
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Nicholas J Audette
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $133,110
- **Award type:** 5
- **Project period:** 2023-07-16 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10888988

## Citation

> US National Institutes of Health, RePORTER application 10888988, To identify mechanisms of predictive processing across the distributed thalamocortical circuit (5K99DC020770-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10888988. Licensed CC0.

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