- Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. ... [Read More]
- Total Size
- 5 files (264 MB)
- Data Citation
- Hussain, M., Grill, W. M., & Pelot, N. A. (2024). Data and scripts from: Highly efficient modeling and optimization of neural fiber responses to electrical stimulation. Duke Research Data Repository. https://doi.org/10.7924/r48g8tf24
- DOI
- 10.7924/r48g8tf24
- Publication Date
- May 30, 2024
- ARK
- ark:/87924/r48g8tf24
- Affiliation
- Publisher
- Type
- Funding Agency
- National Institutes of Health Stimulating Peripheral Activity to Relieve Conditions Initiative
- Grant Number
- OT2 OD025340
- 75N98022C00018
- Contact
- Nicole Pelot PhD.: nikki.pelot@duke.edu, https://orcid.org/0000-0003-2844-0190
- Title
- Data and scripts from: Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.
- Repository
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README.txt | 2024-05-30 | Download | |
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ascent.zip | 2024-05-30 | Download | |
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figures.zip | 2024-05-30 | Download | |
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inputs.zip | 2024-05-30 | Download | |
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sourcedata.zip | 2024-05-30 | Download |