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Computational Neuroscience Lab (Collaborative Unit)
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The lab studies the computational principles of the brain through theory and experiments, and links them to the next generation of AI and brain-machine interfaces (BMI).
AboutAbout the lab
The principles of learning and computation in the brain, from engineering and neuroscience.
Computational Neuroscience (Collaborative Unit) is a collaborative unit set up with ATR, RIKEN, and the Okinawa Institute of Science and Technology Graduate University to train people with a broad perspective on systems science.
Research areas include computational neuroscience, brain-machine interfaces, information processing in local circuits, decoding of neural information, the basal ganglia and neuromodulators, and evolutionary robotics.
Students are mainly supervised by faculty of the Graduate School of Informatics, and receive supplementary research guidance from collaborating faculty at the other institutions.
Key terms
- Computational neuroscience
- A field that uncovers how the brain works using mathematical models and computational concepts
- Brain-machine interface
- Technology that connects the brain and machines directly
- Bayesian inference
- A method of inference that updates beliefs based on probability as new information is obtained
- Neurofeedback
- A method of changing how the brain is active while viewing information about one's own brain activity
ResearchResearch themes
At each partner institution, the lab studies the brain and AI, information processing in neural circuits, and the mechanisms of machines and brains that learn flexibly.
*Illustrative imageLinking the Brain and AI: Brain-Machine Interfaces (ATR)
The lab reads out information from human brain activity signals and uses it to manipulate the brain in order to understand how the brain works. Using neurofeedback, which returns the decoded information as a reward, it aims to treat psychiatric disorders and more.
*Illustrative imagePrinciples of Information Processing in Neural Circuits (RIKEN)
The lab seeks to uncover the computational principles of the brain underlying memory and decision-making. By integrating models of neural circuits with models of Bayesian inference in the brain, it investigates how neural information is represented.
*Illustrative imageMachines and Brains That Learn Flexibly (Okinawa Institute of Science and Technology Graduate University)
Groups working on robot experiments, brain science experiments, and learning theory collaborate to address two questions: the engineering problem of how to build machines that can learn as flexibly as humans and animals, and the neuroscience problem of how the brain's flexible learning is realized.
MembersFaculty
- Affiliated Professor川人 光男
- Affiliated Professor銅谷 賢治
- Affiliated Associate Professor磯村 拓哉
- Affiliated Associate ProfessorKANG Louis
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Nyt-Cajal DRAWING m1673-resized-for-web” Santiago Ramón y Cajal / Public domain (cropped)
- Linking the Brain and AI: Brain-Machine Interfaces (ATR): “Brain-computer interface experiment” Laurens R. Krol / CC BY 4.0 (cropped)
- Principles of Information Processing in Neural Circuits (RIKEN): “Multicolor fluorescence image of a living PC-12 cell” 8x57is / CC BY-SA 4.0 (cropped)
- Machines and Brains That Learn Flexibly (Okinawa Institute of Science and Technology Graduate University): “OIST Campus at Night ( )” OIST from Onna Village, Japan / CC BY 2.0 (cropped)
- Related lab (計算論的認知神経科学
（連携ユニット） ): “Anatomical model of human brain, Wellcome L0010009” Wellcome Images / CC BY 4.0 (cropped) - Related lab (脳情報学 分野): “EEG Recording Cap” Chris Hope / CC BY 2.0 (cropped)
- Related lab (認知情報学 分野): “Human eye iris 1” Kamil Saitov / CC BY 4.0 (cropped)
- Related lab (情報セキュリティ
（連携ユニット） ): “Credit Card Chip ( )” kuhnmi / CC BY 2.0 (cropped)
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