Organic Nervetronics for Next-Generation Computing and Nerveprosthetics

Real-time information processing is an important feature of biological nerves. To implement such properties (event-driven, parallel operation, etc.) artificially, organic nervetronics have been upsurged. Furthermore, artificial synapses can be combined with sensors and actuators to emulate the functions of biological sensory and motor neurons with a simple circuit structure. In addition, it consumes 10^-6 times less energy in operation than a CMOS-based von Neumann computing system. This organic neural electron can become a new strategy for soft robotics, next-generation computing, and neuroprosthetics by imitating biological neuroplastic events which can replace the damaged nerves.<br/>First, we controlled the microstructure of various organic semiconductors, and blended two diketopyrrolopyrrole (DPP) based semiconductor with different side chain to achieve long-term plasticity. Also, by producing trapping site for ions, ion-gel gated artificial synapses showed extremely long-term retention time.<br/>Furthermore, we developed an artificial nerve that emulates biological afferent and efferent nerve. The connection of biological organs and artificial afferent nerves shows a hybrid reflex arc that shows future applicability for neural prostheses. The movement of the detached cockroach leg can be controlled by external sensory information. Also, an artificial auditory system was developed by integrating a triboelectric nanogenerator an artificial synapse. The optoelectronic artificial sensorimotor nerve emulates optogenetically engineered neurons and synapses. The contraction of artificial muscles well imitates biological muscle contraction. The stretchable nervetronics reproduced coordinated bipedal movement and practical motions such as ‘kicking a ball’ and ‘walking/running’ in living animals. Neuroplasticity-based artificial nerves can be not only used for next-generation computing systems but also restore the biological afferent and efferent nerve response which can pave a new way to future neuromorphic computing.