Organic Neuromorphic Electronics for Local Learning and Control in Robotics

Biological systems learn by interacting directly with the environment and receiving positive and negative feedback from many different stimuli that influence the formation of dynamic associations inside their neuronal systems. These associations are encoded within the neuronal connections of the brain, specifically in the strength of the synaptic connection between neu-rons. Recently, the organic electrochemical transistor (OECT) has emerged for its use as artifi-cial synapse showcasing volatile and non-volatile tunable dynamics that emulate synaptic plas-ticity. This allows the mapping of (artificial) neural networks into hardware-based circuits as well as the development of specialized neuromorphic chips mimicking the efficient structure and function of the human brain. Organic neuromorphic electronics also operate on low voltage which make them ideal for integration in energy-restricted environments such as autonomous robots [1-6].<br/>In this work, we aim to create a small-scale locally trained organic neuromorphic circuit con-nected to a robot. The robot is able to respond to different input signals adaptively and form behavioral associations based on its interaction with the environment. This on-chip adaptable integration of multiple stimuli with low-voltage organic neuromorphic electronics opens the way towards stand-alone, brain-inspired circuitry in autonomous and intelligent robotics.<br/>References<br/>[1] C. Mead, Neuromorphic electronic systems (1990).<br/>[2] E. O. Neftci et al., Nat Mach Intell, 1, 3 (2019).<br/>[3] P. Gkoupidenis et al., Advanced Materials, 27, 44 (2015).<br/>[4] Y. van de Burgt et al., Nature Materials, 16, 4 (2017).<br/>[5] A. Melianas et al., Science Advances, 6, 27 (2020).<br/>[6] I. Krauhausen et al., Science Advances, 7, 50, (2021).