High level abstraction of memristor model for neural network simulation

Memristor emerged as an auspicious device in the field of neuromorphic engineering due to its nanoscale size, non-volatility, scalability, fast switching, low power consumption, high density and compatability with CMOS technology. This paper unveils the first mathematical memristor modeling in C ++. We also represent the implementation and training of a single layer and multilayer neural network using C++ memristor model. The memristive crossbar structure has been utilized to train the network. We successfully demonstrated linear and non-linear seperable logic functions using C++ memristor modeling in the simulation of neural network. We also demonstrated pattern classifier using single layer neural network at two different learning rates and the network performs satisfactorily at both the learning rates.

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