Author

Aren Hite

University of Michigan

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:electronic health records· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Cynthia A. ChestekiD· 2 shared Hisham TemmariD· 2 shared Luis Hernan CubillosiD· 2 shared Madison M. KelbermaniD· 2 shared Matthew J. MenderiD· 2 shared Matthew S. WillseyiD· 2 shared Parag G. PatiliD· 2 shared Anne DraelosiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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2 shared papersNeuroscience

Cynthia A. ChestekORCID

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2 papers

Recent·Most cited

Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance

Luis Hernan Cubillos, Madison M. Kelberman, Matthew J. Mender, Aren Hite, Hisham Temmar, Matthew S. Willsey, Nishant Ganesh Kumar, Theodore A. Kung, Parag G. Patil, Cynthia A. Chestek, Chandramouli Krishnan

This study evaluates if brain and muscle synergies improve decoding performance in implanted brain-machine interfaces (iBMIs) using dimensionality reduction techniques, finding that they do not enhance performance or generalization.

Restorative Neurology and Neuroscience · Jun 22, 2026Peer-reviewedNeuroscience

Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces

Luis Hernan Cubillos, Guy Revach, Matthew J. Mender, J Costello, Hisham Temmar, Aren Hite, Diksha Zutshi, Dylan M Wallace, Xiaoyong Ni, Madison M. Kelberman, Matthew S. Willsey, Ruud J. G. van Sloun, Nir Shlezinger, Parag G. Patil, Anne Draelos, Cynthia A. Chestek

This study compares KalmanNet, an explainable BMI decoder, with deep-learning and Kalman filter models, finding it achieves comparable performance while offering interpretability.

neural information processing systems · Jan 1, 2024Peer-reviewedNeuroscience