Implementation of a spike-based perceptron learning rule using TiO2-x memristors

32Citations
Citations of this article
79Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Mostafa, H., Khiat, A., Serb, A., Mayr, C. G., Indiveri, G., & Prodromakis, T. (2015). Implementation of a spike-based perceptron learning rule using TiO2-x memristors. Frontiers in Neuroscience, 9(OCT). https://doi.org/10.3389/fnins.2015.00357

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free