Neuromorphic computing using spike-based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. In this work, the dynamic operating conditions of NbO2-based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks.

中文翻译：

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用于基于突发的感知器的基于 NbO2 的忆阻神经元

使用基于尖峰学习的神经形态计算在降低计算能力方面具有广阔的前景。由两个局部有源忆阻器组成的忆阻神经元已被用于模拟生物神经元的动力学行为。在这项工作中，全面研究了基于 NbO2 的忆阻神经元的动态运行条件及其在尖峰和爆发之间的转换边界。此外，还分析了突发的潜在机制，并证明了每个突发期间尖峰数量的可控性。最后，提出了利用每个突发周期的尖峰数对信息进行编码的感知器神经网络中的模式分类和信息传输。