Neural Coding and Brain Computing Unit (Tomoki Fukai)

Overview

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

  • Thomas Burns' work "Simplicial Hopfield networks" has been accepted for presentation at ICLR2023. Congratulations! (January 2023)
  • Congratulations to Dr. Chi-Chung Fung for being offered a tenure-track position at the City University of Hong Kong! (March 2022)
  • Congratulations to Dr. Hongjie Bi for being offered a tenure-track position at Shenzhen Bay Laboratory! (March 2022)
  • Congratulations to Roman Koshkin being selected for the Google AI PhD Fellowship! (September 2021)
  • Congratulations to Dr. Menezes Carvalho for defending her PhD at the University of Tokyo! (March 2021)

More Unit News

Postdoc Opportunities

A small number of postdoc positions are available in computational neuroscience and the related theoretical fields (brain-inspired AI, machine learning in neuroscience, etc.). The lab explores the neural mechanisms of the brain functions such as learning, memory, probabilistic inference, and recently language processing through computational modeling and experimental data analyses. The keywords of our recent studies are:

1) single-neuron and network models of learning and memory;

2）computation with spikes;

3) dendritic computation;

4) computational roles of spontaneous activity;

5）analysis of behavioral and large-scale neural data (rodents, songbirds) related to these topics.

The behavioral data analysis involves a recent joint project with Yazaki Unit at OIST in which we will explore social communications among songbirds during song learning.

We welcome applications from motivated candidates with a background in theoretical/computational sciences. Communication and writing skills in English but not in Japanese are required. Applications will be considered on a rolling basis.

For details of the job posting, please refer to the OIST Careers page.