Author

Masa-aki Sato

Advanced Telecommunications Research Institute InternationalORCID 0000-0003-4366-7289Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:imaging· 3 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Mitsuo KawatoiD· 3 shared Huu HoangiD· 2 shared Keisuke ToyamaiD· 2 shared Akiko Nishio· 1 shared Hidehiko KomatsuiD· 1 shared Kazuo KitamuraiD· 1 shared Masanobu KanoiD· 1 shared Miki Hashizume· 1 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersNeuroscience

Mitsuo KawatoORCID

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3 papers

Recent·Most cited

Precise hyperacuity estimation of spike timing from calcium imaging

Huu Hoang, Masa-aki Sato, Shigeru Shinomoto, Shinichiro Tsutsumi, Miki Hashizume, Tomoe Ishikawa, Masanobu Kano, Yuji Ikegaya, Kazuo Kitamura, Mitsuo Kawato, Keisuke Toyama

This paper introduces HA_time, a hyperacuity algorithm for precise spike timing estimation from calcium imaging, achieving up to 20-fold improvement in spike time precision.

Scientific Reports · Oct 20, 2020Peer-reviewedNeuroscience

Computational model for human 3D shape perception from a single specular image

Takeaki Shimokawa, Akiko Nishio, Masa-aki Sato, Mitsuo Kawato, Hidehiko Komatsu

This study develops a computational model to estimate 3D shapes of specular objects from a single image, showing high accuracy and similarity to human perception errors.

Frontiers in Computational Neuroscience · Mar 1, 2019Peer-reviewedNeuroscience

Hyperacuity Bayesian methods to enhance temporal resolution of two-photon recording of the complex spikes in the cerebellar Purkinje cells

Huu Hoang, Masa-aki Sato, Mitsuo Kawato, Keisuke Toyama

This paper introduces two Bayesian algorithms to improve spike detection and timing precision in two-photon recordings using ground-truth electrical signals, outperforming unsupervised methods in both experimental and simulated data.

bioRxiv · Nov 16, 2017 · PreprintNeuroscience