HRL Laboratories, 3011 Malibu Canyon Rd, Malibu, CA, 90265, USA.
Nat Commun. 2018 Nov 7;9(1):4661. doi: 10.1038/s41467-018-07052-w.
Neuromorphic networks of artificial neurons and synapses can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. For image processing, silicon neuromorphic processors outperform graphic processing units in energy efficiency by a large margin, but deliver much lower chip-scale throughput. The performance-efficiency dilemma for silicon processors may not be overcome by Moore's law scaling of silicon transistors. Scalable and biomimetic active memristor neurons and passive memristor synapses form a self-sufficient basis for a transistorless neural network. However, previous demonstrations of memristor neurons only showed simple integrate-and-fire behaviors and did not reveal the rich dynamics and computational complexity of biological neurons. Here we report that neurons built with nanoscale vanadium dioxide active memristors possess all three classes of excitability and most of the known biological neuronal dynamics, and are intrinsically stochastic. With the favorable size and power scaling, there is a path toward an all-memristor neuromorphic cortical computer.
人工神经元和突触的神经形态网络可以解决计算上困难的问题，其能效是冯·诺依曼架构无法达到的。对于图像处理，硅神经形态处理器在能效方面大大优于图形处理单元，但芯片级吞吐量却低得多。硅处理器的性能-效率困境可能无法通过摩尔定律对硅晶体管的缩放来克服。可扩展和仿生主动忆阻器神经元和被动忆阻器突触为无晶体管神经网络提供了自足的基础。然而，以前忆阻器神经元的演示仅显示了简单的积分-点火行为，并没有揭示生物神经元的丰富动态和计算复杂性。在这里，我们报告了使用纳米级二氧化钒主动忆阻器构建的神经元具有兴奋性的三个类别和大多数已知的生物神经元动态，并且具有内在的随机性。随着有利的尺寸和功率缩放，有望实现全忆阻器神经形态皮质计算机。
