AI Bibliography
AI Bibliography |
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Yada, Y., Yasuda, S., & Takahashi, H. (2021). Physical reservoir computing with force learning in a living neuronal culture. Applied Physics Letters, 119(17), 173701. |
| Resource type: Journal Article BibTeX citation key: Yada2021 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Decision Theory, Engineering, General, Medical science, Nanotechnology Subcategories: Autonomous systems, Edge AI, Machine intelligence, Machine learning, Nanotechnology, Neural nets, Neurosymbolic, Quantum computing, Synthetic intelligence Creators: Takahashi, Yada, Yasuda Publisher: Collection: Applied Physics Letters |
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Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir computing (PRC). However, PRC that generates a coherent signal output from a spontaneously active neuronal system is still challenging. To overcome this difficulty, we here constructed a closed-loop experimental setup for PRC of a living neuronal culture, where neural activities were recorded with a microelectrode array and stimulated optically using caged compounds. The system was equipped with first-order reduced and controlled error learning to generate a coherent signal output from a living neuronal culture. Our embodiment experiments with a vehicle robot demonstrated that the coherent output served as a homeostasis-like property of the embodied system from which a maze-solving ability could be generated. Such a homeostatic property generated from the internal feedback loop in a system can play an important role in task solving in biological systems and enable the use of computational resources without any additional learning.
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