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000873814 1001_ $$0P:(DE-Juel1)131022$$aSiemon, A.$$b0$$eCorresponding author
000873814 245__ $$aAnalyses of a 1-layer neuromorphic network using memristive devices with non-continuous resistance levels
000873814 260__ $$aMelville, NY$$bAIP Publ.$$c2019
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000873814 520__ $$aThe emerging nonvolatile memory technology of redox-based resistive switching (RS) devices is not only a promising candidate for future high density memories but also for computational and neuromorphic applications. In neuromorphic as well as in memory applications, RS devices are configured in nanocrossbar arrays, which are controlled by CMOS circuits. With those hybrid systems, brain-inspired artificial neural networks can be built up and trained by using a learning algorithm. First works on hardware implementation using relatively large and high current level RS devices are already published. In this work, the influence of small and low current level devices showing noncontinuous resistance levels on neuromorphic networks is studied. To this end, a well-established physical-based Verilog A model is modified to offer continuous and discrete conduction. With this model, a simple one-layer neuromorphic network is simulated to get a first insight and understanding of this problem using a backpropagation algorithm based on the steepest descent method
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000873814 7001_ $$0P:(DE-HGF)0$$aFerch, S.$$b1
000873814 7001_ $$0P:(DE-Juel1)174220$$aHeittmann, A.$$b2
000873814 7001_ $$0P:(DE-Juel1)131022$$aWaser, R.$$b3
000873814 7001_ $$00000-0002-6766-8553$$aWouters, D. J.$$b4
000873814 7001_ $$0P:(DE-Juel1)158062$$aMenzel, S.$$b5
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