Neural research: A leap forward with hydrogel technology and modular networks
01/14/2025
AIMR-led innovations bridge spatial and temporal gaps in neuronal network studies

Understanding the relationship between the structure and function of biological neuronal networks is key to uncovering how specific architectures influence network behavior in healthy and diseased states—providing insights into brain functionality and disorders.
To this end, researchers are actively looking for a way to manipulate neuronal networks into a device that enables the recording of neural activities with both spatial and temporal precision.
“Traditional microelectrode arrays can record neural activities with high temporal precision, but they ignore fine spatial details,” explains Hideaki Yamamoto, a member of an AIMR research team. “We wanted to find a recording method that can also provide exquisite spatial information.”
In a 2023 article1, Yamamoto, Ayumi Hirano-Iwata and co-workers achieved this objective by combining microfluidic cell engineering with high-density microelectrode arrays (HD-MEAs).
“In this work, our strategy was to use a hydrogel coating technique to seamlessly integrate microfluidic devices with HD-MEAs,” says Yamamoto. “This approach stabilized the construction of complex neuronal architectures on HD-MEAs that facilitated neural-activity measurements, while providing precise structural information on the neuronal networks.”
The demonstration of a neuronal-network manipulation and recording method capable of providing precise information on both structure and activity has expanded the team into a fifteen-university research consortium, including AIMR, RIEC, Oita University, Waseda University, and Future University Hakodate. Funded by the MEXT Grant-in-Aid for Transformative Research Areas (A) program, the consortium is entitled “Multicellular Neurobiocomputing2.”
A recent result from the consortium explored the possibility of replacing artificial neural networks in reservoir-based predictive coding with biologically inspired neuronal network models3.
(Author: Patrick Han)
References
- Sato Y., Yamamoto H., Kato H., Tanii T., Sato S. and Hirano-Iwata A. Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks Frontiers in Neuroscience 16, 943310 (2023). | article
- Project page
- Sato Y., Yamamoto H., Ishikawa Y., Sumi T., Sono Y., Sato S., Katori Y. and Hirano-Iwata A. In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays Japanese Journal of Applied Physics 63, 108001 (2024). | article
This research highlight has been approved by the authors of the original article and all information and data contained within has been provided by said authors.