Implementation of a spike-based perceptron learning rule using TiO2-x memristors.
Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.
Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
Hesham
H
Mostafa
Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.
Ali
A
Khiat
Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.
Alexander
A
Serb
Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
Christian G
CG
Mayr
Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
Giacomo
G
Indiveri
Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.
Themis
T
Prodromakis
learning; memristors; neuromorphic architectures; perceptron; silicon neurons; synaptic plasticity