Physical reservoir computing with FORCE learning in a living neuronal culture
Publication/Creation DateOctober 26 2021
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.
Date archivedNovember 15 2021
Last editedNovember 15 2021
How to cite this entry
Hirokazu Takahashi, Yuichiro Yada, Shusaku Yasuda. (October 26 2021). "Physical reservoir computing with FORCE learning in a living neuronal culture". Applied Physics Letters. AIP Publishing LLC. Fabric of Digital Life. https://fabricofdigitallife.com/Detail/objects/5549