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Logical Life Science Lab
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A lab that approaches the mysteries of life and intelligence through information science.
AboutAbout the lab
Seeing life and intelligence through learning.
The Logical Life Science Lab aims to approach the mysteries of "life" and "intelligence" by integrating life science and information science.
Both life and intelligence are "learning systems" that live by remaking themselves to fit their surrounding environment.
The lab studies a wide range of subjects, including the human mind and decision making, the behavior of cells, audiovisual information processing and motor control in robots, and the analysis of social networks.
Key terms
- Reinforcement Learning
- A machine learning method that learns how to act through repeated trial and error so as to maximize reward.
- Super-Resolution
- Obtaining images with finer detail than the performance of the optical system allows, through information processing.
- Bayesian Statistics
- A statistical approach that gradually updates beliefs expressed as probabilities, based on observed data.
ResearchResearch themes
The lab studies the modeling of decision making and brain processes, systems neurobiology, Bayesian super-resolution and image information representation, and statistical bioinformatics.
*Illustrative imageUnderstanding Decision Making through Machines and the Brain
The lab seeks to explain human decision making by integrating information engineering and cognitive science. It develops reinforcement learning methods that adapt to complex environments and applies them to AI and robots, and it tests whether the brain implements them by measuring brain activity.
*Illustrative imageHow the Brain Processes Information
The lab investigates, from the standpoint of information theory, how information is encoded in networks of neurons, and models how neurons form networks during development. It uses methods from biophysics and information science.
*Illustrative imageSeeing the Invisible with Bayesian Methods
To measure biopolymers and other targets precisely, the lab studies super-resolution methods, which obtain resolution beyond the performance of the optical system through information processing, using Bayesian statistics and hypothesis testing. It also studies the visualization of information related to human cognition and sensibility.
*Illustrative imageDecoding Large-Scale Biological Data
The lab develops mathematical statistics techniques for handling high-dimensional biological data such as brain activity measurements and gene expression data. By extracting meaningful information from noisy data, it explores the functions of intelligence and life.
MembersFaculty
The faculty of the Logical Life Science Lab are Professor Shin Ishii, Associate Professor Hideaki Shimazaki, Assistant Professor Risa Katayama, and Program-Specific Assistant Professors Tien Cuon Phi and Rui Gong.
- ProfessorShin Ishii
- Associate ProfessorHideaki Shimazaki
- Assistant ProfessorRisa Katayama
- Program-Specific Assistant ProfessorPHI Tien Cuon
- Program-Specific Assistant ProfessorGONG Rui
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: “Page showing illustration of the brain Wellcome L0041062” Wellcome Images / CC BY 4.0 (cropped)
- Understanding Decision Making through Machines and the Brain: “Rl agent” Notfruit / CC BY-SA 4.0 (cropped)
- How the Brain Processes Information: “Cultured Rat Hippocampal Neuron ( )” ZEISS Microscopy from Germany / CC BY 2.0 (cropped)
- Seeing the Invisible with Bayesian Methods: “SK8-18-2 human derived cells, fluorescence microscopy ( )” ZEISS Microscopy from Germany / CC BY 2.0 (cropped)
- Decoding Large-Scale Biological Data: “Normalized Linkage Heat Map” CP2000A / CC BY-SA 4.0 (cropped)
- Related lab (Electrical Information Systems Lab): “Chubu Electric Power Grid Chushin Substation” photo: Qurren (talk) Taken with Canon PowerShot G9 X / CC BY-SA 4.0 (cropped)
- Related lab (Earth Atmosphere Measurement / Atmospheric Precision Measurement and Diagnosis Lab): “MU Radar, Research Institute for Sustainable Humanosphere, Kyoto University - Oct 15, 2011” eiko / CC BY-SA 2.0 (cropped)
- Related lab (Biocybernetics Lab (formerly Biomedical Engineering)): “Flavia Saraiva 2016 Olympics Test Event event finals 1” Miriam Jeske / CC BY 3.0 br (cropped)
- Related lab (Photonic Quantum Information Engineering Lab): “FMN Lab single photon detector” FMNLab / CC BY 4.0 (cropped)
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