Takuya Isomura

Takuya Isomura, Ph.D.

Unit Leader, Brain Intelligence Theory Unit
takuya.isomura [at] riken.jp

Research Overview

The intelligence of biological organisms is explained by optimization. Biological organisms recognize the surrounding environment by optimizing their internal representations or hypotheses about how the hidden dynamics and causes generate sensory inputs—and they optimize their behavior to adapt to the environment by sampling preferred inputs, thereby increasing the probability of survival and reproduction. Our research aims to mathematically express universal characterization of their intelligence in terms of biologically plausible neuronal circuits and synaptic plasticity rules.

Main Research Fields


Related Research Fields

Interdisciplinary Science & Engineering / Mathematical & Physical Sciences / Biological Sciences / Informatics/Human informatics/Intelligent informatics


Selected Publications

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

  1. Isomura, T. & Toyoizumi, T.
    "Dimensionality reduction to maximize prediction generalization capability."
    Nature Machine Intelligence 3(5), 434-446 (2021).
  2. Isomura, T. & Toyoizumi, T.
    "On the achievability of blind source separation for high-dimensional nonlinear source mixtures."
    Neural Computation 33(6), 1433-1468 (2021).
  3. Isomura, T. & Friston, K. J.
    "Reverse-engineering neural networks to characterize their cost functions."
    Neural Computation 32, 2085-2121 (2020).
  4. Isomura, T., Parr, T. & Friston, K. J.
    "Bayesian filtering with multiple internal models – towards a theory of social intelligence."
    Neural Computation 31, 2390-2431 (2019).
  5. Isomura, T. & Toyoizumi, T.
    "Multi-context blind source separation by error-gated Hebbian rule."
    Scientific Reports 9, 7127 (2019).
  6. Isomura, T. & Friston, K. J.
    "In vitro neural networks minimise variational free energy."
    Scientific Reports 8, 16926 (2018).
  7. Isomura, T. & Toyoizumi, T.
    "Error-gated Hebbian rule: a local learning rule for principal and independent component analysis."
    Scientific Reports 8, 1835 (2018).
  8. Isomura, T. & Toyoizumi, T.
    "A local learning rule for independent component analysis."
    Scientific Reports 6, 28073 (2016).
  9. * Isomura, T., Kotani, K. & Jimbo, Y.
    "Cultured cortical neurons can perform blind source separation according to the free-energy principle."
    PLoS Computational Biology 11(12), e1004643 (2015).

Lab Members

Principal investigator

Takuya Isomura
Unit Leader