Organic neuromorphic electronics for bio-inspired processing and local sensorimotor learning in robotics (Conference Presentation)

Abstract

Artificial intelligence applications have demonstrated their enormous potential for complex processing over the last decade. However, they are mainly based on digital operating principles while being part of an analogue world. Moreover, they still lack the efficiency and computing capacity of biological systems. Neuromorphic electronics emulate the analogue information processing of biological nervous systems. Neuromorphic electronics based on organic materials have the ability to emulate efficiently and with fidelity a wide range of bio-inspired functions. A prominent example of a neuromorphic device is based on organic mixed conductors (ionic-electronic). Neuromorphic devices based on organic mixed conductors show volatile, non-volatile and tunable dynamics suitable for the emulation of synaptic plasticity and neuronal functions, and for the mapping of artificial neural networks in physical circuits. Finally, small-scale organic neuromorphic circuits enable the local sensorimotor control and learning in robotics.