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• W1830722006 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." @default.
• W1830722006 created "2016-06-24" @default.
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• W1830722006 date "2015-10-02" @default.
• W1830722006 modified "2023-10-03" @default.
• W1830722006 title "Implementation of a spike-based perceptron learning rule using TiO2−x memristors" @default.
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• W1830722006 doi "https://doi.org/10.3389/fnins.2015.00357" @default.
• W1830722006 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4591430" @default.
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