  With the development of research on memristor, memristive neural networks
(MNNs) have become a hot research topic recently. Because memristor can mimic
the spike timing-dependent plasticity (STDP), the research on STDP based MNNs
is rapidly increasing. However, although state-of-the-art works on STDP based
MNNs have many applications such as pattern recognition, STDP mechanism brings
relatively complex hardware framework and low processing speed, which block
MNNs' hardware realization. A non-STDP based unsupervised MNN is constructed in
this paper. Through the comparison with STDP method on the basis of two common
structures including feedforward and crossbar, non-STDP based MNNs not only
remain the same advantages as STDP based MNNs including high accuracy and
convergence speed in pattern recognition, but also better hardware performance
as few hardware resources and higher processing speed. By virtue of the
combination of memristive character and simple mechanism, non-STDP based MNNs
have better hardware compatibility, which may give a new viewpoint for
memristive neural networks' engineering applications.
