Paper Info
Title
Memristor-based Synaptic Networks and Logical Operations Using In-Situ Computing
Abstract
We present new computational building blocks based on memristive devices. These blocks, can be used to implement either supervised or unsupervised learning modules. This is achieved using a crosspoint architecture which is an efficient array implementation for nanoscale two-terminal memristive devices. Based on these blocks and an experimentally verified SPICE macromodel for the memristor, we demonstrate that firstly, the Spike-Timing-Dependent Plasticity (STDP) can be implemented by a single memristor device and secondly, a memristor-based competitive Hebbian learning through STDP using a $1\times 1000$ synaptic network. This is achieved by adjusting the memristor's conductance values (weights) as a function of the timing difference between presynaptic and postsynaptic spikes. These implementations have a number of shortcomings due to the memristor's characteristics such as memory decay, highly nonlinear switching behaviour as a function of applied voltage/current, and functional uniformity. These shortcomings can be addressed by utilising a mixed gates that can be used in conjunction with the analogue behaviour for biomimetic computation. The digital implementations in this paper use in-situ computational capability of the memristor.
Year
DOI
Venue
2011
10.1109/ISSNIP.2011.6146610
CoRR
Keywords
Field
DocType
resistance,logic gates,memristors,plasticity,hebbian theory,materials science,hebbian learning,switches,emerging technology,unsupervised learning
Memristor,Logic gate,Computer science,Spice,Electronic engineering,Unsupervised learning,Hebbian theory,Memistor,Memory architecture,Computation,Distributed computing
Journal
Volume
Citations 
PageRank 
abs/1108.4182
7
1.29
References 
Authors
4
7
Name
Order
Citations
PageRank
Omid Kavehei127331.47
Said F. Al-Sarawi214917.94
Kyoung-Rok Cho321731.77
Iannella, N.4929.85
Sung-Jin Kim593.04
Kamran Eshraghian610127.54
Derek Abbott7567.08