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Brain Mathematics and Information Collaboration Lab
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The lab uses mathematical models to uncover how the brain learns and adapts.
AboutAbout the lab
Uncovering through theory how the brain learns
The Brain Mathematics and Information Collaboration Lab studies how the brain processes information by analyzing mathematical models in the field of computational neuroscience.
The lab investigates the mechanisms by which neural circuits adapt to their environment and learn.
It combines analytical techniques developed in statistical mechanics and information theory with computer simulation.
The lab aims to understand the basic principles that neural networks need in order to function well.
Key terms
- Computational neuroscience
- A field that expresses brain function as computational models and seeks to understand it in mathematical terms
- Neural plasticity
- The phenomenon in which neurons change their properties in response to their own activity; it underlies learning, memory, and development
- Neural network
- A network formed by connected neurons that processes information
NewsLab news
Research result announced: “Advances in biological information processing involve ‘leaps’”
Research result announced: “Theoretical elucidation of the mechanism that compresses the learning process into a small amount of synthetic data”
Research result announced: “A neural circuit model that extracts important information”
Research result announced: “The strength of synaptic connections is involved in the mechanism that keeps the amount and quality of sleep constant”
A Research Fellow received the Toshihiko Tokizane Memorial Neuroscience Award for Outstanding Doctoral Research
ResearchResearch themes
Through the analysis of mathematical models, the lab studies the mechanisms of information processing in the brain and how neural circuits adapt to their environment and learn. By combining analytical techniques from statistical mechanics and information theory with computer simulation, it aims to understand the basic principles that neural networks need in order to function properly.
*Illustrative imageTheory of Information Processing and Adaptation in the Brain
Using computational models, the lab investigates how information is processed in the brain and how brain circuits adapt to their environment. With analytical methods from statistical physics and information theory, it explores the key properties behind how neural circuits work, and organizes a wide range of experimental results into a few central concepts.
*Illustrative imageTheory of Neural Plasticity
Neural plasticity, in which neurons change their own properties in response to their activity, plays a major role in learning, memory, and development. Using mathematical models, the lab aims to build a theory of plasticity that encompasses findings from the cellular level to the circuit level.
*Illustrative imagePredicting the Performance of the Resulting Circuits
From the structure and behavior of neural circuits shaped by plasticity, the lab predicts what performance they have in representing and retaining information.
MembersFaculty
The members of the Brain Mathematics and Information Collaboration Lab consist of faculty, postdoctoral researchers, graduate students, research students, and assistants.
- Affiliated ProfessorTaro Toyoizumi
- Affiliated Associate ProfessorLukas Ian Schmitt
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: “Synapsin and CamK2 positive neurons” ManuelSchottdorf / CC BY-SA 4.0 (cropped)
- Theory of Information Processing and Adaptation in the Brain: “White Matter Connections Obtained with MRI Tractography” Xavier Gigandet et. al. / CC BY 2.5 (cropped)
- Theory of Neural Plasticity: “A segment of pyramidal cell dendrite from stratum radiatum (CA1)” Dr. Kristen M. Harris / CC BY 2.0 (cropped)
- Predicting the Performance of the Resulting Circuits: “Lorenz-1000” Vogel (К.Б. Циберкин) / Public domain (cropped)
- Related lab (Systems Pharmacology Lab): “Lab mouse mg 3263” Rama / CC BY-SA 2.0 fr (cropped)
- Related lab (Mathematical Bioinformatics Lab): “Mouse Embryo Cerebellum ( )” NIH Image Gallery from Bethesda, Maryland, USA / Public domain (cropped)
- Related lab (Brain Information Measurement and Control Lab (Amano-Nakayama Lab)): “Double cone coil” Baburov / CC BY-SA 4.0 (cropped)
- Related lab (Learning Mathematical Informatics Lab (Yamanishi Lab)): “CERN Server 03” Florian Hirzinger - www.fh-ap.com / CC BY-SA 3.0 (cropped)
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