Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems¹. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics² and brain-machine interfaces³. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs^4,5 that can both directly interface with living tissue and adapt based on biofeedback^6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

通过模仿生物系统¹的分布式信息处理，灵感来自大脑的计算范例已在视觉和语言任务的自动化方面取得了实质性进步。人工神经网络（ANN）与生物系统之间的相似性启发了人工神经网络在包括假肢²和脑机接口³在内的生物医学界面中的实现。尽管有希望，但这些实现依靠软件来运行ANN算法。最终，需要构建既可以与活组织直接交互又可以基于生物反馈^6,7进行适应的硬件ANN ^4,5。迈向生物整合的神经形态系统的第一步，是基于生化信号传导活性实现突触调节。在这里，我们直接将有机神经形态装置与多巴胺能细胞耦合，以构成具有神经递质介导的突触可塑性的生物杂交突触。通过模仿突触裂隙的多巴胺回收机制，我们证明了长期调节和突触重量的恢复，为将人工神经形态系统与生物神经网络相结合铺平了道路。