Neural Coding and Brain Computing Unit (Tomoki Fukai)


Cognitive functions of the brain, such as sensory perception, learning and memory, and decision making emerge from computations by neural networks. We consider that the advantages of biological neural computation in comparison with machine computation reside in the way that the brain’s neural circuits implement computation. To uncover neural code and circuit mechanisms of brain computing, we take computational and theoretical approaches. Our goal is to construct a minimal but yet effective description of powerful and flexible computation implemented by the brain’s neural circuits.

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Research interests

  • Network mechanisms of statistical modeling (e.g., predicitve coding, hierarchical Bayesian inference)
  • Computations in idling brain states for memory, reasoning, and beyond
  • Dendritic computation for network-level computing and learning
  • Deciphering neural code in large-scale neural activity data
  • Spike-based learning and computing theories (for neuro-AI)

Latest Unit News

  • Congratulations to Roman Koshkin for his work "convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data " accepted by ICML2024!
  • Tomoki Fukai received JNNS Research Award from the Japanese Neural Network Society.

More Unit News

Postdoc opportunities

We are interested in hiring postdocs with strong background in computational neuroscience, physics, and applied mathematics. Interested candidates are requested to send their resumes to tomoki.fukai[at]
For details of the job posting, please refer to the OIST Careers page.