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Chethan Pandarinath, PhD

Affiliations: 
2003-2011 Electrical Engineering Cornell University, Ithaca, NY, United States 
 2012-2016 Neurosurgery, Electrical Engineering Stanford University, Palo Alto, CA 
 2016- Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, GA, United States 
Area:
neural prosthetics, neural engineering, brain-machine interfaces, motor systems, visual system, retina, neural circuitry, adaptation
Website:
http://snel.gatech.edu
Google:
"Chethan Pandarinath"
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Publications

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Ali YH, Bodkin KL, Rigotti-Thompson M, et al. (2024) BRAND: A platform for closed-loop experiments with deep network models. Journal of Neural Engineering
Lee WH, Karpowicz BM, Pandarinath C, et al. (2024) Identifying distinct neural features between the initial and corrective phases of precise reaching using AutoLFADS. Biorxiv : the Preprint Server For Biology
Chung B, Zia M, Thomas KA, et al. (2023) Myomatrix arrays for high-definition muscle recording. Elife. 12
Shah NP, Avansino D, Kamdar F, et al. (2023) Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex. Biorxiv : the Preprint Server For Biology
Versteeg C, Sedler AR, McCart JD, et al. (2023) Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity. Arxiv
Ali YH, Bodkin K, Rigotti-Thompson M, et al. (2023) BRAND: A platform for closed-loop experiments with deep network models. Biorxiv : the Preprint Server For Biology
Patel AN, Sedler AR, Huang J, et al. (2023) High-performance neural population dynamics modeling enabled by scalable computational infrastructure. Journal of Open Source Software. 8
Batista AP, Pandarinath C, Yu BM. (2023) Krishna Shenoy (1968-2023). Neuron. 111: 764-766
Chung B, Zia M, Thomas K, et al. (2023) Myomatrix arrays for high-definition muscle recording. Biorxiv : the Preprint Server For Biology
Keshtkaran MR, Sedler AR, Chowdhury RH, et al. (2022) A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nature Methods
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