Toshitake Asabuki (朝吹 俊丈)
From Jun 2024, I started my lab at RIKEN Center for Brain Science. We are looking for postdocs and students who are interested in computational neuroscience!
I am leading a lab at RIKEN Center for Brain Science. I am interested in synaptic plasticity as a mechanism for learning across multiple hierarchies in the brain.
Research Interests:
Computational neuroscience, Neural network model, Synptic plasticity,
Curriculum Vitae
Education
Ph.D. Department of Complexity Science and Engineering, The University of Tokyo, 2020
M.S. Department of Complexity Science and Engineering, The University of Tokyo, 2017
B.S. Department of Life Science and Medical Bioscience, Waseda University, 2015
Academic positions
ECL Unit Leader, RIKEN CBS, from Jun 2024 to present
Research associate, Imperial Colloge London, Computatinal Neuroscience Laboratory (Clopath Lab), from May 2022 to May 2024
Post-Doctoral Researcher, Okinawa Institute of Science and Technology Graduate University, Neural Coding and Brain Computing Unit (Fukai unit), from Apr 2020 to May 2022
Grants and awards
Special Research Students, Okinawa Institute of Science and Technology Graduate University, Neural Coding
and Brain Computing Unit, from Apr 2019
Junior Research Associate, RIKEN Center for Brain Science, Laboratory for
Neural Coding and Brain Computing, from Apr 2018 to Mar 2019
IEEE Computational Intelligence Society Japan Chapter Young ResearcherAward, 2017
Publications
Preprints
Asabuki T, Gillon CJ and Clopath C, Learning predictive signals within a local recurrent circuit. (2023) bioRxiv doi:10.1101/2023.06.15.545081
Refereed papers
Asabuki T and Clopath C, Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity. (2023) bioRxiv doi: 10.1101/2023.05.01.538909 (accepted in eLife)
Asabuki T and Fukai T, Learning rules for cortical-like spontaneous replay of an internal model. (2023) bioRxiv doi: 10.1101/2023.02.17.528958 (accepted in eLife)
Asabuki T, Kokate P and Fukai T, Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data. PLOS Computational Biology (2022).
Dellaferrera G, Asabuki T and Fukai T, Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons. Frontiers in Neuroscience (2022).
Ohta M*, Asabuki T* and Fukai T, Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models. Scientific Reports (2022).
*equal contribution
Fukai T, Asabuki T and Haga T, Neural mechanisms for learning hierarchical structures of information. Current Opinion in Neurobiology (2021).
Vargas DV and Asabuki T, Continual general chunking problem and SyncMap. Association for the Advancement of Artificial Intelligence (2021).
Asabuki T and Fukai T, Somatodendritic consistency check for temporal feature segmentation. Nature Communications, 11:1554 (2020).
Asabuki T, Hiratani T and Fukai T, Interactive reservoir computing for chunking information streams. PLOS Computational Biology, 14(10):e1006400 (2018).
Martin-Vazquez G*, Asabuki T*, Isomura T and Fukai T, Learning task-related activities from independent local-field-potential components across motor cortex layers. Frontiers in Neuroscience, 12:429 (2018).
*equal contribution
Contact Information
Email
toshitake.asabuki[at]riken.jp
Affiliation
Hierarchical Neural Computation RIKEN ECL Research Unit
RIKEN Center for Brain Science
2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan