A biohybrid synapse with neurotransmitter-mediated plasticity
A biohybrid synapse with neurotransmitter-mediated plasticity
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DOI：
10.1038/s41563-020-0703-y
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发表时间：
2020-06-15
期刊：
影响因子：
41.2
通讯作者：
Santoro, Francesca
中科院分区：
文献类型：
--
作者：
Keene, Scott T.;Lubrano, Claudia;Santoro, Francesca
一种在微流体系统中连接有机神经形态设备的细胞培养，通过由生物突触中使用的神经递质多巴胺的释放介导的化学反应，可逆地改变设备突触重量。受大脑启发的计算范例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实质性的进步(1)。人工神经网络(ANN)与生物系统的相似性启发了ANN在包括假肢(2)和脑机接口(3)在内的生物医学接口中的实现。虽然前景看好，但这些实现依赖于软件来运行ANN算法。最终，希望构建既能与活组织直接对接又能基于生物反馈(6，7)进行适应的硬件神经网络(4，5)。走向生物集成神经形态系统的第一个基本步骤是实现基于生化信号活动的突触调节。在这里，我们直接将有机神经形态装置与多巴胺能细胞偶联，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循环机制，我们展示了突触重量的长期调节和恢复，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。
A cell culture interfacing an organic neuromorphic device in a microfluidic system reversibly modifies the device synaptic weight through chemical reactions mediated by the release of dopamine, a neurotransmitter used in biological synapses.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(1). The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics(2)and brain-machine interfaces(3). 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.