Memristor Networks

  title={Memristor Networks},
  author={Andrew Adamatzky and Leon Ong Chua},
  booktitle={Springer International Publishing},
Using memristors one can achieve circuit functionalities that are not possible to establish with resistors, capacitors and inductors, therefore the memristor is of great pragmatic usefulness. Potential unique applications of memristors are in spintronic devices, ultra-dense information storage, neuromorphic circuits and programmable electronics. Memristor Networks focuses on the design, fabrication, modelling of and implementation of computation in spatially extended discrete media with many… 
Sensitivity analysis of memristors based on emulation techniques
  • K. OchsEnver Solan
  • Engineering
    2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS)
  • 2016
This work uses memristor emulators, based on the wave digital method, for sensitivity analysis, and gets a reproducible investigation method, which can be used in real circuits, before fabricating the real device.
Switching Synchronization and Metastable States in 1D Memristive Networks
It is shown that metastable transmission lines composed of metastable memristive circuits can be used to transfer the information from one space location to another and the triad of memory networks functionalities in their 1D networks: information processing, storage and transfer.
Special issue on ‘Advances in Memristive Networks’
This Special Issue on ‘Advances in Memristive Networks’ is particularly devoted to present the most recent results as well as key aspects and perspectives of on-going research on relevant topics, all of them involving large networks of memristor devices used in diverse applications.
Experimental verification of a memristive neural network
An electronic circuit able to emulate the behavior of a neural network based on memristive synapses is presented, built with two flux-controlled floating memristor emulator circuits operating at high frequency and two passive resistors.
Experimental Study of Artificial Neural Networks Using a Digital Memristor Simulator
A fully digital implementation of a memristor hardware (HW) simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors, which demonstrates very good matching with the mathematical model on which it is based.
Memristors as Candidates for Replacing Digital Potentiometers in Electric Circuits
This paper has analyzed and implemented tunable circuits such as a voltage divider, an inverting amplifier, a high-pass filter, and a phase shifter, and verified that a memristor has equal or better characteristics than a digital potentiometer.
Memristor and Inverse Memristor: Modeling, Implementation and Experiments
Pinched hysteresis is considered to be a signature of the existence of memristive behavior. However, this is not completely accurate. In this chapter, we are discussing a general equation taking into
Stochastic Memristive Interface between Electronic FitzHugh-Nagumo Neurons
The dynamics of memristive device in response to neuron-like signals and coupling electronic neurons via memristive device has been investigated theoretically and experimentally. The simplest
Memristor Models for Machine Learning
The results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing, and proposes two different ways to incorporate it into memristor simulation models.
High-frequency memristive synapses
An analysis of the memristor characteristics in order to obtain a suitable synaptic response is described, showing symmetrical synaptic weighting when the k parameter is close to its maximum value.


The elusive memristor: properties of basic electrical circuits
We present an introduction to and a tutorial on the properties of the recently discovered ideal circuit element, a memristor. By definition, a memristor M relates the charge q and the magnetic flux ϕ
A ferroelectric memristor.
It is demonstrated that voltage-controlled domain configurations in ferroelectric tunnel barriers yield memristive behaviour with resistance variations exceeding two orders of magnitude and a 10 ns operation speed.
A hybrid nanomemristor/transistor logic circuit capable of self-programming
The digitally configured memristor crossbars were used to perform logic functions, to serve as a routing fabric for interconnecting the FETs and as the target for storing information.
Memristive devices and systems
A broad generalization of memristors--a recently postulated circuit element--to an interesting class of nonlinear dynamical systems called memristive systems is introduced. These systems are
Self-organized computation with unreliable, memristive nanodevices
This work proposes to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations, useful for complex pattern recognition problems.
Nanoscale memristor device as synapse in neuromorphic systems.
A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
Simulation of a memristor-based spiking neural network immune to device variations
System level simulations on a textbook case show that performance can compare with traditional supervised networks of similar complexity and show the system can retain functionality with extreme variations of various memristors' parameters, thanks to the robustness of the scheme, its unsupervised nature, and the power of homeostasis.
Memristive switching mechanism for metal/oxide/metal nanodevices.
Experimental evidence is provided to support this general model of memristive electrical switching in oxide systems, and micro- and nanoscale TiO2 junction devices with platinum electrodes that exhibit fast bipolar nonvolatile switching are built.
Spike-timing-dependent learning in memristive nanodevices
  • G. Snider
  • Computer Science
    2008 IEEE International Symposium on Nanoscale Architectures
  • 2008
The key ideas are to factor out two synaptic state variables to pre- and post-synaptic neurons and to separate computational communication from learning by time-division multiplexing of pulse-width-modulated signals through synapses.
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices.