神经形态工程学
突触
计算机科学
突触重量
神经科学
突触可塑性
接口
人工神经网络
多巴胺
神经递质
人工智能
生物
计算机硬件
生物化学
受体
中枢神经系统
作者
Scott T. Keene,Claudia Lubrano,Setareh Kazemzadeh,Armantas Melianas,Yaakov Tuchman,Giuseppina Polino,Paola Scognamiglio,Lucio Cinà,Alberto Salleo,Yoeri van de Burgt,Francesca Santoro
出处
期刊:Nature Materials
[Springer Nature]
日期:2020-06-15
卷期号:19 (9): 969-973
被引量:254
标识
DOI:10.1038/s41563-020-0703-y
摘要
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 ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. 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. 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI