# A framework for developing translatable intelligent neural interface systems for precision neuromodulation therapies

> **NIH NIH R01** · EMORY UNIVERSITY · 2020 · $408,610

## Abstract

Despite advances in neuromodulation technology, therapeutic devices are often ineffective or have adverse
side effects. Next-generation closed-loop neuromodulation systems will provide great potentials for
improving the therapeutic outcome by sensing the neural states and adapting the neuromodulatory actions.
These systems will provide powerful tools for understanding the mechanisms of treatment by elucidating the
causal link between regulating the physiological states and the therapeutic or the behavioral outcomes.
However, a lack of systematic approach to optimally control neurostimulation is a major barrier to fully utilize
their potentials. Furthermore, the real-time implementation of advanced optimization and control algorithms
requires powerful computing hardware that pose a major challenge for translating the effective neural
interface systems into implantable or wearable devices with limited power supply.
The proposed project is addressing these two problems by developing an open-source end-to-end platform,
called NeuroWeaver, to design, test and deploy intelligent Closed-Loop Neuromodulation (iCLON) systems
that automatically can learn the optimal neuromodulation control policies by interacting with the nervous
system. We cast the problem of optimizing neuromodulation into reward-based learning where achieving
the desired neural state or the therapeutic outcome represents a measure of reward for the iCLON system.
We will use techniques from reinforcement learning and model predictive control to develop algorithms that
enable iCLON systems learn the optimal actions to maximize their reward.
Memory dysfunction is one of the most devastating symptoms of Alzheimer’s disease and age-related
dementia. We will develop the NeuroWeaver platform in the context of designing iCLON systems to induce
good memory states in the hippocampus by closed-loop amygdala stimulation. Optimizing the memory-
enhancing effects of amygdala stimulation will have immediate benefits to research on treatments for
memory disorders. More broadly, the NeuroWeaver platform can be combined with a wide range of
biological sensors and actuators to design intelligent closed-loop control systems for regulating
physiological processes far beyond the proposed application in this proposal. Our proposed platform will
have the potential to create an open-source ecosystem for collaboration between machine learning,
neuroscience, and computer architecture communities as well as provide tools for further enrichment of the
algorithms and broader utilization in the biomedical domain.

## Key facts

- **NIH application ID:** 10005329
- **Project number:** 5R01EB028350-02
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Hadi Esmaeilzadeh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $408,610
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005329

## Citation

> US National Institutes of Health, RePORTER application 10005329, A framework for developing translatable intelligent neural interface systems for precision neuromodulation therapies (5R01EB028350-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10005329. Licensed CC0.

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