- 著者
- Hideaki Yamamoto Ayumi Hirano-Iwata Shigeo Sato
- 出版者
- The Japan Society of Applied Physics
- 雑誌
- JSAP Review (ISSN:24370061)
- 巻号頁・発行日
- vol.2023, pp.230420, 2023 (Released:2023-07-06)
- 参考文献数
- 18
The structure and function of complex neuronal networks in the brain can be partially reconstituted in vitro by integrating cell culture and microfluidic device technologies. In this report, we review our recent studies on developing microfluidic devices to reconstitute small neuronal networks bearing a modular structure, which is a canonical structure found in the nervous systems of animals. We also describe the process of recording functional activity from the reconstituted neuronal networks. These fundamental technologies offer novel tools for investigating structure–function relationships in living neuronal networks and exploring the physical basis of biological computing in the brain.
- 著者
- Daisuke Oguchi Satoshi Moriya Hideaki Yamamoto Shigeo Sato
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- Nonlinear Theory and Its Applications, IEICE (ISSN:21854106)
- 巻号頁・発行日
- vol.13, no.2, pp.427-433, 2022 (Released:2022-04-01)
- 参考文献数
- 10
- 被引用文献数
- 1
Reinforcement learning is promising as a machine learning paradigm in edge computing. However, its high computational cost poses a challenge when implementing in devices with limited circuit resources and power consumption. In this study, we investigated the relationship between the bit-length of floating-point operations and the learning performance of the reinforcement learning algorithm. In the case of the FrozenLake maze problem, we found that the learning performance of 8-bit floating-point arithmetic decreased, while that of 16-bit floating-point arithmetic was comparable to that of 64-bit CPU arithmetic. Our results provide a practical guideline for designing a dedicated reinforcement learning hardware with minimum circuit resources and power consumption.