Research Summary

Selected Publications

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

• 1. Isomura, T., Kotani, K., Jimbo, Y. & Friston, K. J.
  "Experimental validation of the free-energy principle with in vitro neural networks"
  Nature Communications 14, 4547 (2023).
• 2. Isomura, T.
  "Active inference leads to Bayesian neurophysiology"
  Neuroscience Research 175, 38-45 (2022).
• 3. Isomura, T., Shimazaki, H. & Friston, K. J.
  "Canonical neural networks perform active inference"
  Communications Biology 5, 55 (2022).
• 4. Isomura, T. & Toyoizumi, T.
  "On the achievability of blind source separation for high-dimensional nonlinear source mixtures"
  Neural Computation 33(6), 1433-1468 (2021).
• 5. Isomura, T. & Toyoizumi, T.
  "Dimensionality reduction to maximize prediction generalization capability"
  Nature Machine Intelligence 3(5), 434-446 (2021).
• 6. Isomura, T. & Friston, K. J.
  "Reverse-engineering neural networks to characterize their cost functions"
  Neural Computation 32, 2085-2121 (2020).
• 7. 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).
• 8. Isomura, T. & Friston, K. J.
  In vitro neural networks minimise variational free energy
  Scientific Reports 8, 16926 (2018).
• 9. Isomura, T. & Toyoizumi, T.
  "A local learning rule for independent component analysis"
  Scientific Reports 6, 28073 (2016).
• 10. *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).

Recent Research Results

• 

  Aug. 8, 2023
  Mathematical theory predicts self-organized learning in real neurons

• 

  Jan. 14, 2022
  The free-energy principle explains the brain

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