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.
Computational neuroscience, Neural network model, Synptic plasticity,
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
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
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
Liu Z, Kurth A, Osako Y, Asabuki T, Learning sculpts orthogonal task manifolds for continual skill learning in recurrent networks. bioRxiv (2026)
Obi-Nagata K, Suzuki N, Kosugi K, Ozawa K, Sasaguri H, Niimi K, Miyamoto K, Takao M, Toda M, Asabuki T, Hayashi-Takagi A, Human-specific fast synaptic kinetics enable rapid detection of predictive features. bioRxiv (2025).
Asabuki T, Clopath C, Taming the chaos gently: a Predictive Alignment learning rule in recurrent neural networks. Nature Communications (2025).
Asabuki T, Gillon CJ, Clopath C, Learning predictive signals within a local recurrent circuit. PNAS (2025).
Asabuki T, Clopath C, Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity. eLife (2024).
Asabuki T, Fukai T, Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences. eLife (2024).
Asabuki T, Kokate P, Fukai T, Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data. PLOS Computational Biology (2022).
Dellaferrera G, Asabuki T, Fukai T, Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons. Frontiers in Neuroscience (2022).
Ohta M*, Asabuki T*, Fukai T, Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models. Scientific Reports (2022).
Fukai T, Asabuki T, Haga T, Neural mechanisms for learning hierarchical structures of information. Current Opinion in Neurobiology (2021).
Vargas DV, Asabuki T, Continual general chunking problem and SyncMap. Association for the Advancement of Artificial Intelligence (2021).
Asabuki T, Fukai T, Somatodendritic consistency check for temporal feature segmentation. Nature Communications, 11:1554 (2020).
Asabuki T, Hiratani T, Fukai T, Interactive reservoir computing for chunking information streams. PLOS Computational Biology, 14(10):e1006400 (2018).
Martin-Vazquez G*, Asabuki T*, Isomura T, Fukai T, Learning task-related activities from independent local-field-potential components across motor cortex layers. Frontiers in Neuroscience, 12:429 (2018).
toshitake.asabuki[at]riken.jp
Hierarchical Neural Computation RIKEN ECL Research Unit
RIKEN Center for Brain Science
2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan