big data computing. By emulating biological nervous systems, this "von Neumann bottleneck" can be solved by using artificial synapses that combine memory and processing functions in one cell, as in biological nervous systems. [1,2] Emulation of biological synapses and nerves also provides the sensing and responding functions with human-like abilities such as event-driven processing and high-accuracy perception, which are limited in conventional human-interactive systems such as e-skins, human-machine interfaces, and neuro-prostheses. [3][4][5][6] In biological nervous systems, sensory signals are evoked by external stimuli (e.g., pressure, light, temperature) and transmitted through sensory nerves in the peripheral nervous system (PNS) to the central nervous system (CNS), that performs perceptual processing of information. Afterward, the processed signal is transmitted along nerves to generate appropriate responses to the stimulus. Neuromorphic systems that emulate biological PNS and CNS may enable energy-efficient operation to process sophisticated real-world problems by detecting and responding to environmental information. The artificial sensory nerves consist of sensors, artificial neurons, and artificial synapses to emulate this perceptual processing. The sensory signals are converted to voltage spikes (i.e., action potentials) by artificial neurons, then processed by artificial synapses. These artificial sensory nerves emulate the event-driven operation of biological nerves and thus consume energy only while responding to inputs. Therefore, artificial nerves consume much less energy than conventional artificial sensory systems, which require periodic scanning of sensing/processing units, and that use standby energy when input is absent. Furthermore, the artificial nerves can be promising for prosthetic applications because they can generate biocompatible spike signals without bulky and rigid external encoding units. [7] After perceptual processing, biological responses can be achieved by stimulating target organs and tissues. Intregration of these neuromorphic systems with biological systems to form so-called "bio-hybrid neuromorphic systems", will realize biomimetic signal transmission and information processing at bio-electronic interfaces. Thus, these systems successfully emulate efficient biological perception processing, that has sensing and responding abilities, and therefore, show promise for use in next-generation healthcare monitoring and neuroprosthetic devices that need to detect bio-signals in a Neuromorphic electronics that emulate biological synapses and nerves can provide a solution to overcome the limitation in energy efficiency of von Neumann computing systems. With increasing demands on bio-medical applications such as healthcare monitoring and neuroprosthetic devices, biohybrid neuromorphic electronics are evaluated as ways to process biological information and replace biological systems. Successful realization of biohybrid neuromorphic systems requires replication of various synaptic pro...