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Implementation of a spike-based perceptron learning rule using TiO2-x memristors.
Front Neurosci 2015; 9:357FN

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.

Authors+Show Affiliations

Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.Nanoelectronics and Nanotechnology Research Group, School of Electronics and Computer Science, University of Southampton UK.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

26483629

Citation

Mostafa, Hesham, et al. "Implementation of a Spike-based Perceptron Learning Rule Using TiO2-x Memristors." Frontiers in Neuroscience, vol. 9, 2015, p. 357.
Mostafa H, Khiat A, Serb A, et al. Implementation of a spike-based perceptron learning rule using TiO2-x memristors. Front Neurosci. 2015;9:357.
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, p. 357. doi:10.3389/fnins.2015.00357.
Mostafa H, et al. Implementation of a Spike-based Perceptron Learning Rule Using TiO2-x Memristors. Front Neurosci. 2015;9:357. PubMed PMID: 26483629.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Implementation of a spike-based perceptron learning rule using TiO2-x memristors. AU - Mostafa,Hesham, AU - Khiat,Ali, AU - Serb,Alexander, AU - Mayr,Christian G, AU - Indiveri,Giacomo, AU - Prodromakis,Themis, Y1 - 2015/10/02/ PY - 2015/06/08/received PY - 2015/09/18/accepted PY - 2015/10/21/entrez PY - 2015/10/21/pubmed PY - 2015/10/21/medline KW - learning KW - memristors KW - neuromorphic architectures KW - perceptron KW - silicon neurons KW - synaptic plasticity SP - 357 EP - 357 JF - Frontiers in neuroscience JO - Front Neurosci VL - 9 N2 - 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. SN - 1662-4548 UR - https://www.unboundmedicine.com/medline/citation/26483629/Implementation_of_a_spike_based_perceptron_learning_rule_using_TiO2_x_memristors_ L2 - https://doi.org/10.3389/fnins.2015.00357 DB - PRIME DP - Unbound Medicine ER -