Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
Tissue Electronics, Istituto Italiano di Tecnologia, Naples, Italy.
Nat Mater. 2020 Sep;19(9):969-973. doi: 10.1038/s41563-020-0703-y. Epub 2020 Jun 15.
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 systems. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics and brain-machine interfaces. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs that can both directly interface with living tissue and adapt based on biofeedback. 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.
脑启发计算范式通过模拟生物系统的分布式信息处理,在视觉和语言任务的自动化方面取得了重大进展。人工神经网络 (ANN) 与生物系统的相似性激发了 ANN 在生物医学接口中的实现,包括假肢和脑机接口。虽然很有前景,但这些实现依赖于软件来运行 ANN 算法。最终,构建能够直接与活体组织接口并基于生物反馈进行自适应的硬件 ANN 是可取的。实现生物集成神经形态系统的第一个基本步骤是基于生化信号活动实现突触调节。在这里,我们将有机神经形态器件与多巴胺能细胞直接耦合,构成具有神经递质介导的突触可塑性的生物混合突触。通过模拟突触间隙中的多巴胺再循环机制,我们证明了突触权重的长期调节和恢复,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。