Abstract
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.
Highlights
Was zero, the robot moved forward; otherwise, the robot turned either right or left
Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir computing (PRC)
The activity of neurons was linearly combined with weight wt, and the value yt 1⁄4 wtxt was used as the output of the neural network
Summary
Cite as: Appl. Phys. Lett. 119, 173701 (2021); https://doi.org/10.1063/5.0064771 Submitted: 27 July 2021 • Accepted: 22 September 2021 • Published Online: 26 October 2021 Yuichiro Yada, Shusaku Yasuda and Hirokazu Takahashi COLLECTIONS Paper published as part of the special topic on Neuromorphic Computing: From Quantum Materials to Emergent Connectivity
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