Deviance detection and regularity sensitivity in dissociated neuronal cultures
These results challenge the traditional view that a hierarchically structured neural network is required to process complex temporal patterns, suggesting instead that deviant detection and regularity sensitivity are inherent properties arising from the primitive neural network. They also suggest new directions for the development of neuro-inspired artificial intelligence systems, emphasizing the importance of incorporating adaptive mechanisms and temporal dynamics in the design of neural networks.
Authors
- Tomoyo Isoguchi Shiramatsu (ORCID: https://orcid.org/0000-0002-5446-3866)
- Dai Akita
- Zhuo Zhang (ORCID: https://orcid.org/0000-0001-7286-4535)
- Zenas C. Chao (ORCID: https://orcid.org/0000-0001-9402-0851)
- Hirokazu Takahashi (ORCID: https://orcid.org/0000-0001-6182-3236)
- Amit Yaron (ORCID: https://orcid.org/0000-0002-5360-5674)
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- Frontiers in Neural Circuits
- Published
- 2025-08-25
- DOI
- https://doi.org/10.3389/fncir.2025.1584322
- Citations
- 1
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.91
Funders
- Japan Agency for Medical Research and Development
- Secom Science and Technology Foundation
- Japan Society for the Promotion of Science