Spiking neural networks exploit spatiotemporal processing, spiking sparsity, and high interneuron bandwidth to maximize the energy efficiency of neuromorphic computing. While conventional silicon-based technology can be used in this context, the resulting neuron-synapse circuits require multiple transistors and complicated layouts that limit integration density. Here, we demonstrate unprecedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiking neuron implementation. These devices employ wafer-scale mixed-dimensional van der Waals heterojunctions consisting of chemical vapor deposited monolayer molybdenum disulfide and solution-processed semiconducting single-walled carbon nanotubes to emulate the spike-generating ion channels in biological neurons. Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking responses including phasic spiking, delayed spiking, and tonic bursting. In addition to neuromorphic computing, the tunable Gaussian response has significant implications for a range of other applications including telecommunications, computer vision, and natural language processing.

尖峰神经网络利用时空处理，尖峰稀疏性和较高的中间神经元带宽来最大化神经形态计算的能量效率。尽管可以在这种情况下使用传统的基于硅的技术，但最终的神经元突触电路需要多个晶体管和复杂的布局，从而限制了集成密度。在这里，我们展示了双门高斯异质结晶体管的前所未有的静电控制，可简化尖峰神经元的实现。这些设备采用晶片级混合尺寸的范德华异质结，包括化学气相沉积的单层二硫化钼和溶液处理的半导体单壁碳纳米管，以模拟生物神经元中产生尖峰的离子通道。基于这些双门高斯器件的电路可以实现多种生物尖峰响应，包括相位尖峰，延迟尖峰和强音爆发。除神经形态计算外，可调高斯响应对一系列其他应用（包括电信，计算机视觉和自然语言处理）也具有重要意义。