The brain is capable of massively parallel information processing while consuming only ∼1–100 fJ per synaptic event^1,2. Inspired by the efficiency of the brain, CMOS-based neural architectures³ and memristors^4,5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10³ μm² devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems^6,7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

大脑能够进行大规模的并行信息处理，而每个突触事件^1,2仅消耗约1–100 fJ 。受大脑效率的启发，基于CMOS的神经架构³和忆阻器^4,5正在开发用于模式识别和机器学习的软件。但是，CMOS架构的易变性，设计复杂性和高电源电压，以及忆阻器的随机和能量成本高昂的转换，使使用这两种方法实现大脑的互连性，信息密度和能量效率的路径变得复杂。在这里，我们描述了一种与现有忆阻器有着根本不同机制的电化学神经形态有机器件（ENODe）。e节点切换在低电压和能量（<10 PJ 10 ³ 微米^2个装置），显示>内的500个不同的，非挥发性的电导状态〜1 V范围，并在神经网络仿真中实现时，可实现较高的分类精度。塑料ENODes也被制造在柔性基板上，从而能够在可拉伸的电子系统^6,7中整合神经形态功能。机械灵活性使ENODes与三维体系结构兼容，从而开辟了通往与人脑可比的极端互连的道路。