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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Major research interests

  • Mechanisms of hierarchical probabilistic computation by the brain
  • Roles of dendrites in network-level learning and computation
  • Circuit mechanisms of memory information processing
  • Deciphering neural code in large-scale neural activity data
  • Creating brain-inspired technologies for AI

Latest Unit News

  • Congratulations to Roman Koshkin being selected for the Google AI PhD Fellowship! (September 2021)
  • Congratulations to Dr. Menezes Carvalho for defending her PhD at Tōdai! (March 2021)
More Unit News

Postdoc Opportunities

A small number of postdoc positions are available in computational neuroscience and related fields (e.g., brain-inspired AI, machine learning in neuroscience). The followings are the keywords of our research interests:

  1. network mechanisms of learning and memory
  2. computation with spikes
  3. the computational role of dendrites
  4. the role of spontaneous activity in brain computing

We are also interested in expanding the scope of our research based on the findings. Applications from interested candidates are highly welcome.