Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1-4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6-8. Using both experiments and modelling, here we show how multiple electrophysical processes-including Mott transition dynamics-form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

中文翻译：

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用于神经形态工程的三阶纳米电路元件

当前用于仿生或神经形态人工智能的硬件方法依赖于精细的晶体管电路来模拟生物功能。然而，这些可以通过自然表达神经形态非线性动力学的高阶电路元件更忠实地模拟1-4。在电路元件中生成神经形态动作电位理论上需要最少的三阶复杂性（例如，三个动态电物理过程）5，但很少有二阶神经形态元件的例子，而且之前没有任何孤立的三阶复杂性的证明。订购元素6-8。使用实验和建模，我们在这里展示了多个电物理过程（包括莫特跃迁动力学）如何形成纳米级三阶电路元件。我们演示了简单的三阶元素无晶体管网络，这些网络执行布尔运算并找到计算困难的图形分割问题的模拟解决方案。这项工作为实现非常紧凑且功能密集的神经形态计算原语以及神经科学模型的节能验证铺平了道路。