# A Long-range Recurrent Neural Network Mediates Threat Induced Innate Sensorimotor Integrations

> **NIH NIH R21** · UNIVERSITY OF WYOMING · 2022 · $216,750

## Abstract

Summary
An animal’s innate behavior responses to aversive stimuli involves multisensory integration of sensory
information to guide the decision-making process. The long-term goal is to deepen our fundamental knowledge
of the neural mechanisms underlying sensorimotor integration that underlies threat-induced defensive behavior.
We will first focus on functional role of an innate tri-synaptic, long-range recurrent neural network (RNN) linking
emotional regions with somatic motor cortex. Several forebrain regions, including the thalamus, amygdala and
frontal cortical regions, is involved in expression of fear-related behaviors and memories. Understanding how
these three regions interact is critical to deciphering the basic mechanisms of fear-triggered behavior. Recently,
we discovered that a long-range RNN, which is formed by a self-feedback connectivity in the mPFC (Hidden
Unit, HU), integrates inputs from upstream emotional regions (Input Unit, IU, which includes basal lateral
amygdala, insular cortex) and further innervates motor cortex projecting neurons (Output Unit, OU). RNN is a
network with self-feedback (closed-loop) connections. Recurrent circuits are capable of amplification, pattern
completion and memory. The central hypothesis to be examined is that the innate long-range RNN, which is
composed of: fear related emotional centers located in the basal lateral amygdala (BLA) and insular cortex (IC,
i.e., a threat detection unit), decision making hidden unit in mPFC (i.e., a fear integration unit), and a downstream
action initiation center located in the somatic motor cortex (sMO, i.e., a fear output unit), plays a key role in fear
induced sensorimotor integration underlying defensive behavior. Understanding neuronal dynamics in the three
key nodes of the RNN during innate defensive behavior, is crucial for deciphering the role of the RNN in threat-
avoidance behavior selections. A specific aim with two comprehensive sub-aims are proposed to test this
hypothesis: Aim 1. To determine if the RNN circuits are involved in innate defensive behavior. This aim will test
the central hypothesis by: Sub-aim 1a. Examining the neuronal activity dynamics and temporal relationships in
the Input Unit (IU, i.e. BLA), the hidden unit (HU, i.e. mPFC) and Output Unit (OU, i.e. sMO) during defensive
avoidance behavior. Sub-aim 1b. Testing the sufficiency and necessity of activation of the key node of the RNN,
HU and the final output node of the RNN: sMOs, in mediating defensive avoidance behavior. Successful
execution of this exploratory proposal will help us obtain proof-of-concept data that will enable the pursuit of a
full-scale project aimed at understanding the key role of mPFC in sensorimotor integration, and in particular the
role of RNNs as fundamental computation units in emotion-related decision making.

## Key facts

- **NIH application ID:** 10539071
- **Project number:** 1R21MH131363-01
- **Recipient organization:** UNIVERSITY OF WYOMING
- **Principal Investigator:** Qian-Quan Sun
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $216,750
- **Award type:** 1
- **Project period:** 2022-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10539071

## Citation

> US National Institutes of Health, RePORTER application 10539071, A Long-range Recurrent Neural Network Mediates Threat Induced Innate Sensorimotor Integrations (1R21MH131363-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10539071. Licensed CC0.

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