Many of the current artificial intelligence (AI) applications that are rapidly becoming indispensable in our society rely on software-based artificial neural networks or deep learning algorithms that are powerful, but energy-inefficient. The brain in comparison is highly efficient at similar classification and pattern finding tasks. Neuromorphic engineering attempts to take advantage of the efficiency of the brain by mimicking several crucial concepts to efficiently emulate AI tasks. Organic electronic materials have been particularly successful in mimicking both the basic functionality of the brain, including important spiking phenomena, but also in low-power operation of hardware-implemented artificial neural networks as well as interfacing with physiological environments due to their biocompatible nature. This article provides an overview of the basic functional operation of the brain and its artificial counterparts, with a particular focus on organic materials and devices. We highlight efforts to mimic brain functions such as spatiotemporal processing, homeostasis, and functional connectivity and emphasize current challenges for efficient neuromorphic computing applications. Finally, we present our view of future directions in this exciting and rapidly growing field of organic neuromorphic devices.

当前在我们社会中迅速变得不可缺少的许多人工智能（AI）应用都依赖于功能强大但能耗低的基于软件的人工神经网络或深度学习算法。相比之下，大脑在执行类似的分类和模式查找任务时非常高效。神经形态工程学试图通过模仿几个关键概念来有效地模拟AI任务，从而利用大脑的效率。有机电子材料在模仿大脑的基本功能（包括重要的尖峰现象）方面特别成功，而且由于其生物相容性，在硬件实现的人工神经网络的低功耗操作以及与生理环境的接口方面也非常成功。本文概述了大脑及其人造对手的基本功能操作，特别关注有机材料和设备。我们重点介绍了模仿大脑功能（如时空处理，动态平衡和功能连接性）的努力，并强调了有效神经形态计算应用程序当前面临的挑战。最后，我们介绍了在这个令人兴奋且迅速发展的有机神经形态装置领域中对未来方向的看法。