  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.
