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Perceptual and Cognitive Neuroscience Lab
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The lab seeks a quantitative understanding of human brain activity using predictive models.
AboutAbout the lab
Quantitatively understanding human brain function
The lab studies the neural basis of the complex and diverse brain functions that support daily life.
It builds predictive models that explain brain activity recorded with fMRI and electrophysiological recordings, aiming to quantitatively understand information processing and information representation in the brain.
As applications of these predictive models, the lab also aims to develop the mathematical foundations for brain decoding and for communication methods built on it.
The lab belongs to the Division of Brain and Neural Engineering. It operates as part of the Graduate School of Frontier Biosciences and as the Nishimoto Group at the National Institute of Information and Communications Technology (NICT), and members from diverse backgrounds work together on its research.
Key terms
- fMRI
- A method that uses magnetism to record brain activity as images
- Predictive model
- A computational mechanism that predicts how the brain will be active in response to a given stimulus or task
- Brain decoding
- Reading out what a person was seeing or thinking at a given moment from recordings of their brain activity
ResearchResearch themes
The lab investigates the neural basis of the complex and diverse brain functions that support daily life. It builds predictive models that explain brain activity under natural perception and cognition conditions, aiming to quantitatively understand information processing and information representation in the brain.
*Illustrative imagePredictive models that explain brain activity
The lab explains brain activity recorded under natural perception and cognition conditions using predictive models. As applications of these models, it also aims to develop the mathematical foundations for brain decoding and for communication methods.
*Illustrative imageModel building with machine learning
Advances in machine learning, including deep learning and natural language processing, have made it possible to build models that predict brain activity with higher accuracy. The lab believes that applying these advances will yield new quantitative findings and interpretations about information processing and information representation in the brain.
MembersFaculty
The lab's members are faculty, project researchers, technical staff, and administrative assistants.As of 2026
- ProfessorShinji Nishimoto
- Associate ProfessorYushi Sugimoto
- Assistant ProfessorTomoyuki Namima
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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: “FEAT” Bravegirl2003 / CC BY-SA 3.0 (cropped)
- Predictive models that explain brain activity: “XBio illustration – Functional Magnetic Resonance Imaging (fMRI)” explorebiology / CC BY 4.0 (cropped)
- Model building with machine learning: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Related lab (Biofunctional Molecular Measurement Lab): “Fluorecence Microscope-Köhler-Zeiss-1908” Carl Zeiss AG / Public domain (cropped)
- Related lab (Human Evolution and Functional Genomics Lab): “Primate skull series with legend cropped-ar” Primate_skull_series_with_legend_cropped.png: *Primate_skull_series_with_legend.png: Christopher Walsh, Harvard Medical School derivative work: Gary2863 (talk) derivative work: باسم / CC BY 2.5 (cropped)
- Related lab (Control and Information Systems Lab): “Blackghost” Paul Esparon, Sunil Shah, Dr Timothy Nickels and the 2008 CAUV team. / CC BY-SA 3.0 (cropped)
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