# From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain

@article{Snider2011FromST, title={From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain}, author={Greg Snider and Rick Amerson and Dick Carter and Hisham Abdalla and Muhammad Shakeel Qureshi and Jasmin L{\'e}veill{\'e} and Massimiliano Versace and Heather Ames and Sean Patrick and Ben Chandler and Anatoli Gorchetchnikov and Ennio Mingolla}, journal={Computer}, year={2011}, volume={44}, pages={21-28} }

In a synchronous digital platform for building large cognitive models, memristive nanodevices form dense, resistive memories that can be placed close to conventional processing circuitry. Through adaptive transformations, the devices can interact with the world in real time.

## 101 Citations

Synapse behavior characterization and physical mechanism of a TiN/SiOx/p-Si tunneling memristor device

- ChemistryJournal of Materials Chemistry C
- 2019

The demand for massive deep learning neural networks has driven the development of nanoscale memristor devices, which perform brain-inspired neuromorphic computing.

Memristive devices and circuits for computing, memory, and neuromorphic applications.

- Computer Science
- 2012

Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2012.

Circuit proposition for copying the value of a resistor into a memristive device supported by HSPICE simulation

- EngineeringArXiv
- 2013

A circuit for copying the value of a given resistor into a memristive device is proposed and HSPICE simulations are presented to confirm the efficiency of the proposed circuit.

Evolving nanoscale associative memories with memristors

- Computer Science2011 11th IEEE International Conference on Nanotechnology
- 2011

This paper considers the problem of designing associative memories using nano-scale memristors and designs two complementary evolutionary frameworks for the automated discovery of circuits that exploits the analog, time-dependent properties of memristor, resulting in more efficient and simpler designs.

Implementation of memristive neural networks with spike-rate-dependent plasticity synapses

- Computer Science2014 International Joint Conference on Neural Networks (IJCNN)
- 2014

The spike-rate-dependent plasticity (SRDP) of the synapse, an extended protocol of the Hebbian learning rule, is originally implemented by the circuit and some advanced neural activities including learning, associative memory and forgetting are realized based on the SRDP rule.

A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

- Computer ScienceCircuits Syst. Signal Process.
- 2020

Based on the proposed associative memory rules, a memristor neural network with plasticity synapses is designed, which can perform analog operations similar to its biological behavior and construct a relatively simple Pavlov’s dog experiment simulation circuit which can effectively reduce the complexity and power consumption of the network.

Simulation of a memristor-based spiking neural network immune to device variations

- Computer ScienceThe 2011 International Joint Conference on Neural Networks
- 2011

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.

Memristors can implement fuzzy logic

- Computer ScienceArXiv
- 2011

This work proposes implementing fuzzy logic using memristor circuits that may be applicable for instance in fuzzy classifiers and discusses computational power of such circuits with respect to m-efficiency and experimentally observed behavior of memristive devices.

A Unified Learning Framework for Memristive Neuromorphic Hardware

- Computer Science
- 2011

This paper characterizes a subset of local learning laws amenable to implementation in memristive hardware that belong to four broad classes: Hebb rule derivatives with various methods f or gating learning and decay; Threshold rule variations including the covariance and BCM families; Input reconstruction-based learning rules; and Explicit temporal trace-based rules.

A method for automatic tuning the memristance of memristive devices with the capacity of applying to memristive memories

- Chemistry2012 International Conference on Computer Systems and Industrial Informatics
- 2012

The proposed method is based on the sliding mode control and numerical simulations show that it can be used for tuning the memristance of memristive devices with a high accuracy.

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