Titlebook: Memristor Networks; Andrew Adamatzky,Leon Chua Book 2014 Springer International Publishing Switzerland 2014 Computation.Dynamics.Logic.Mem

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Spike-Timing-Dependent-Plasticity with Memristors, with permission.) Here we present a very exciting overlap between emergent nano technology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nano technology devices to the biological synaptic update rule known as Spike-Time-Dep

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Evolving Memristive Neural Networks,r neuromorphic computing—biologically-inspired brainlike information processing in hardware. Memristors are analogous to biological synapses; both feature nonvolatile resistance, charge-dependent plastic response to activity, and can provide adaptive learning when coupled with a Hebbian mechanism. I

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A Memristor-Based Chaotic System with Boundary Conditions,or with boundary conditions. Specifically, charge-controlled and flux-controlled memristor models with appropriate boundary conditions are introduced and the relation between the charge through and the flux across the memristor is derived. The rich and interesting dynamical behaviors of the memristi

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Organic Memristive Devices and Neuromorphic Circuits,This paper is dedicated to organic memristive devices—elements that were designed and constructed for mimicking the most important properties of synapses, responsible for Hebbian type of learning. We will consider the architecture of the device and its properties, as well as circuits and networks wi

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Memristive in Situ Computing,nd physical realizations of two-terminal oxide-based resistive memory was discovered in early 2008, and has since been intensively studied. This class of memory elements is called memristive devices, which includes resistive random access memories (RRAM), phase change memories (PCM) and spin-transfe

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