ArticleFrontiers in neuroscience2015
Implementation of a spike-based perceptron learning rule using TiO2-x memristors.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Run-off election-based decision method for the training and inference process in an artificial neural network.Scientific reports · 2021Article
- Soft eSkin: distributed touch sensing with harmonized energy and computing.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations.Materials (Basel, Switzerland) · 2019Review
- Neuromorphic computing with multi-memristive synapses.Nature communications · 2018Article
- Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype.Frontiers in neuroscience · 2018Article
- Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.Frontiers in neuroscience · 2018Article
- Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses.Nature communications · 2016Article
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Authors and funding
6 authors.
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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.
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