Toward Controlling a Cyber-Physical System using Synthetic Biological Intelligence
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Artificial Intelligence (AI) tools such as ChatGPT have transformed our daily life. Usually, these AI tools are based on Neural Networks (NNs) implemented in digital hardware, i.e., not biological or physical NNs like those found in human brains. Instead, NNs are typically implemented as software on top of a Graphics Processing Unit (GPU) or specialized AI hardware. In contrast, this work-in-progress utilizes a biological NN, demonstrating the use of Synthetic Biological Intelligence (SBI) by implementing a toy example - controlling an inverted pendulum. Biological NNs can be beneficial compared to GPUs or specialized AI hardware, especially in terms of energy efficiency and capability on noisy or dynamic tasks. Following the encoding, decoding, reward, and punishment nature of the biological NN, this work presents initial results toward controlling the physical system in future research.
Details
| Original language | English |
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| Title of host publication | NanoCom 2025 - Proceedings of the 12th ACM International Conference on Nanoscale Computing and Communication |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 157-158 |
| Number of pages | 2 |
| ISBN (electronic) | 9798400721663 |
| Publication status | Published - 22 Oct 2025 |
| Peer-reviewed | Yes |
Conference
| Title | 12th ACM International Conference on Nanoscale Computing and Communication |
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| Abbreviated title | NanoCom 2025 |
| Conference number | 12 |
| Duration | 23 - 25 October 2025 |
| Website | |
| Location | University of Electronic Science and Technology of China |
| City | Chengdu |
| Country | China |
External IDs
| ORCID | /0000-0001-8469-9573/work/198590419 |
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| ORCID | /0000-0001-5410-6810/work/198594991 |