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10.3389/fnins.2015.00357 http://scihub22266oqcxt.onion/10.3389/fnins.2015.00357 C4591430!4591430!26483629     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Neurosci 2015 ; 9 (ä): ä Nephropedia Template TP {{tp|p=26483629|t=2015. Implementation of a spike-based perceptron learning rule using TiO2?x memristors |pdf=|usr=}}{{26483629}} gab.com Text Implementation of a spike-based perceptron learning rule using TiO2 x memristors JO: Front Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26483629 #FOAvip 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. Twit Text FOAVip Implementation of a spike-based perceptron learning rule using TiO2 x memristors ????Front Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26483629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26483629 #FOAvip English Wikipedia <ref>{{Cite pmid|26483629}}</ref> Implementation of a spike-based perceptron learning rule using TiO2?x memristors #MMPMID26483629 Mostafa H; Khiat A; Serb A; Mayr CG; Indiveri G; Prodromakis T Front Neurosci 2015[]; 9 (ä): ä PMID26483629show ga 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. äImplementation of a spike-based perceptron learning rule using TiO2?x (epmc).pdf DeepDyve Pubget Overpricing