Single-Transistor Neuron with Excitatory–Inhibitory Spatiotemporal Dynamics Applied for Neuronal Oscillations
Single-Transistor Neuron with Excitatory–Inhibitory Spatiotemporal Dynamics Applied for Neuronal Oscillations
复制标题
具有兴奋性-抑制性时空动力学的单晶体管神经元应用于神经元振荡
DOI：
10.1002/adma.202207371
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发表时间：
2022
影响因子：
29.4
通讯作者：
Bin Yu
中科院分区：
文献类型：
--
作者：
Hanxi Li;Jiayang Hu;Anzhe Chen;Chenhao Wang;Li Chen;Feng Tian;Jiachao Zhou;Yuda Zhao;Jinrui Chen;Yi Tong;Kian Ping Loh;Yang Xu;Yishu Zhang;Tawfique Hasan;Bin Yu
大脑启发的神经形态计算系统有可能推动下一波人工智能，需要一系列超越简单特征的关键组件。一个新兴的研究趋势是实现先进的功能与超紧凑的神经形态设备。在这项工作中，一个单晶体管神经元被证明可以实现兴奋-抑制（E-I）时空整合和一系列基本的神经元行为。神经元振荡，神经元通信的基本模式，构建高维群体代码以实现大脑中的高效计算，也可以通过神经元晶体管来证明。高度可扩展的E-I神经元可以成为实现核心神经元电路基序和大规模架构计划的基本构建块，以复制节能神经计算，从而形成未来集成神经形态系统的基础。
Brain‐inspired neuromorphic computing systems with the potential to drive the next wave of artificial intelligence demand a spectrum of critical components beyond simple characteristics. An emerging research trend is to achieve advanced functions with ultracompact neuromorphic devices. In this work, a single‐transistor neuron is demonstrated that implements excitatory–inhibitory (E–I) spatiotemporal integration and a series of essential neuron behaviors. Neuronal oscillations, the fundamental mode of neuronal communication, that construct high‐dimensional population code to achieve efficient computing in the brain, can also be demonstrated by the neuron transistors. The highly scalable E–I neuron can be the basic building block for implementing core neuronal circuit motifs and large‐scale architectural plans to replicate energy‐efficient neural computations, forming the foundation of future integrated neuromorphic systems.