Author

Kensuke Arai

Boston University

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· 3 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Assay:electronic health records· 4 Organism:rat· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:Bayesian inference· 1 Research Focus:cell-cell communication· 1 Research Focus:deconvolution· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Uri T. EdeniD· 4 shared Ali YousefiiD· 2 shared Loren M. FrankiD· 2 shared Daniel Fan Liu· 1 shared Emery N. BrowniD· 1 shared Karoline E. Weber· 1 shared Long TaoiD· 1 shared Mohammad R. RezaeiiD· 1 shared

Activity by Year

2018-2020 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 16 mutual co-author links

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4 shared papersNeuroscience

Uri T. EdenORCID

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Author profile →

4 papers

Recent·Most cited

Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models

Mohammad R. Rezaei, Kensuke Arai, Loren M. Frank, Uri T. Eden, Ali Yousefi

The paper proposes a computationally efficient approximate point process filter using mixture models for real-time decoding of neural spiking activity in multidimensional spaces.

Journal of Neuroscience Methods · Nov 21, 2020Peer-reviewedBioinformatics

Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

Reza Saadati Fard, Kensuke Arai, Uri T. Eden, Emery N. Brown, Ali Yousefi

The paper introduces a new framework for analyzing neural synchrony across brain regions using state-space coherence, applied to study task-related neural activity in a Stroop task.

bioRxiv · Jul 13, 2020 · PreprintNeuroscience

Marked point process filter for clusterless and adaptive encoding-decoding of multiunit activity

Kensuke Arai, Daniel Fan Liu, Loren M. Frank, Uri T. Eden

The paper introduces a Bayesian encoding model for real-time adaptation of neural activity decoding in rats during maze exploration.

bioRxiv · Oct 13, 2018 · PreprintNeuroscience

A common goodness-of-fit framework for neural population models using marked point process time-rescaling

Long Tao, Karoline E. Weber, Kensuke Arai, Uri T. Eden

The paper develops a goodness-of-fit framework for neural population models using marked point process time-rescaling, applicable to both spike-sorted and clusterless models.

Journal of Computational Neuroscience · Oct 1, 2018Peer-reviewedNeuroscience