Learning in memristive neural networks with various connection patterns
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Abstract:
Due to the useful properties of nonvolatile，memory and nanoscale，memristors have prospective promising applications in artificial networks，pattern recognition and signal processing.This paper exploits the learning rule of the memristive network based on spiking timing dependent plasticity (STDP) and uses the genetic algorithms with self-adaptation and variable topologies，which allows the number of hidden neurons，connection weights，and connectivity pattern to change self-adaptably.Three memristor models are respectively used as the synapse in the network，including HP linear memristor，non-linear memristor and threshold memristor.The comparison of the performance of the three memristive neural networks is presented，and the hybrid memristive networks’ learning effects are analyzed.
