Toshitake Asabuki, Ph.D.

Research Overview

The brain can flexibly learn from new experiences by integrating them with prior knowledge. This requires hierarchically organizing information to form rich representations of the external world. Our lab investigates the neural circuit mechanisms behind this by building mathematical models of synaptic plasticity that enables multiple interconnected neural circuits to collaboratively learn. We aim to deeply understand the computational principles of the brain's remarkable learning abilities by using computational models to replicate diverse experimental findings.

Main Research Fields

Interdisciplinary Science & Engineering

Related Research Fields

Engineering / Informatics / Complex Systems / Mathematical & Physical Sciences / Biological Sciences / Computational brain science / Learning / Information processing

Keywords

• Computational neuroscience
• Synaptic plasticity
• Neural network

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

1. * Asabuki, T. and Clopath, C.:
   "Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity."
   eLife, 13:RP95243. (2024)
   10.7554/eLife.95243.1
2. * Asabuki, T., Kokate, P., and Fukai, T.:
   "Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data."
   PLOS Computational Biology, 18(6): e1010214. (2022)
   10.1371/journal.pcbi.1010214
3. Asabuki, T and Fukai, T.:
   "Somatodendritic consistency check for temporal feature segmentation."
   Nature Communications, 11:1554. (2020).
   10.1038/s41467-020-15367-w
4. Asabuki, T., Hiratani, T., and Fukai, T.:
   "Interactive reservoir computing for chunking information streams."
   PLOS Computational Biology, 14(10): e1006400. (2018)
   10.1371/journal.pcbi.1006400