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Neuromorphic devices and architectures offer novel ways of data manipulation and processing, especially in data intensive applications. At a single device level, various forms of neuroplasticity have been emulated over the past years, mainly with inorganic devices. The implementation of neuroplasticity functions with these devices also enabled applications at a circuit level related to machine learning such as feature or pattern recognition. Although the field of organic-based neuromorphic devices and circuits is still at its infancy, organic materials may offer attractive features for neuromorphic engineering. Over the past years for example, a few simple neuromorphic functions have been demonstrated with biological substances and bioelectronic devices. In this work various neuromorphic devices will be presented that are based on organic mixed conductors, materials that are traditionally used in organic bioelectronics. A prominent example of a device in bioelectronics that exploits mixed conductivity phenomena is the organic electrochemical transistor (OECT). Devices based on OECTs show volatile and tunable dynamics suitable for the emulation of short-term synaptic plasticity functions. Chemical synthesis allows for the introduction of non-volatile phenomena suitable for long-term memory functions. The device operation in common electrolyte permits the definition of spatially distributed multiple inputs at a single device level. The presence of a global electrolyte in an array of devices also allows for the homeostatic or global control of the array. Global electrical oscillations can be used as global clocks that frequency-lock the local activity of individual devices in analogy to the global oscillations in the brain. Finally, “soft” interconnectivity through the electrolyte can be defined, a feature that paves the way for parallel interconnections between devices with minimal hard-wired connections.
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