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Aonishi Lab
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The lab uses a shared mathematical framework to understand brains, batteries, and optical computers alike.
AboutAbout the lab
Universal understanding based on system isomorphism
The Aonishi Lab studies the brain and nervous system, functional materials such as batteries, and coherent Ising machines, a type of optical computer.
These may seem unrelated at first glance, but as systems they share a common mathematical structure (isomorphism), which means the same machine learning and statistical mechanics methods can be applied to them.
The lab works with experimental and theoretical researchers from many fields to build models and clarify the higher-order functions and design principles of each system.
The lab sees its role as creating a counter-current to the increasing fragmentation of research fields.
Key terms
- Isomorphism
- The property of objects that look different but have the same structure when viewed mathematically
- Coherent Ising machine
- A type of optical computer that uses laser oscillation to search for the optimal answer among a very large number of combinations
- Compressed sensing
- A mathematical technique for reconstructing the original signal or image from a small amount of measurement data
NewsLab news
Paper by Project Assistant Professor Zhang et al. published in Sensors
Paper by Professor Aonishi et al. published in IEEE Access
Paper by Professor Aonishi et al. published in Journal of the Physical Society of Japan
Paper by Project Assistant Professor Zhang et al. published in IEEE Transactions on Circuits and Systems for Video Technol…
Paper by Professor Aonishi et al. published in Journal of Applied Physics
ResearchResearch themes
The lab aims to understand different targets, such as neural systems, functional materials, and optical computers, based on their isomorphism as systems. By applying shared machine learning and statistical mechanics methods across fields, it draws out universal understanding from these different targets.
*Illustrative imageAnalysis of imaging data from neural systems and materials
When the process that generates the data is expressed as a mathematical model, most data from new imaging techniques developed in neuroscience and materials science turn out to be isomorphic. The lab proposes effective analysis methods for problems where machine learning has not yet been used.
*Illustrative imageApplying nonequilibrium statistical mechanics of information across fields
Neural systems, magnetic thin films, and optical oscillators can all be described by the same standard model of nonlinear oscillators. The lab has proposed a method for implementing compressed sensing on a coherent Ising machine and is clarifying its performance limits.
MembersFaculty
The members of the Aonishi Lab are faculty, doctoral and master's students (including students who are working professionals), and technical assistants.As of 2026
- ProfessorToru AonishiPublications ↗
- Assistant ProfessorNaoya Mamada
- Project Assistant ProfessorLiyun Zhang
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: “The Galaxy Within” Dchordpdx / CC BY 4.0 (cropped)
- Analysis of imaging data from neural systems and materials: “Multicolor fluorescence image of a living PC-12 cell” 8x57is / CC BY-SA 4.0 (cropped)
- Applying nonequilibrium statistical mechanics of information across fields: “IR Optical Parametric Oscillator” Kkmurray / CC BY 3.0 (cropped)
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