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Nanotube devices based crossbar architecture: toward neuromorphic computing

Nanotechnology, 2010-04, Vol.21 (17), p.175202-175202

2010

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Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

Neural computation, 2013-02, Vol.25 (2), p.549-566

2013

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Addition of HfO2 interface layer for improved synaptic performance of phase change memory (PCM) devices

Solid-state electronics, 2013-01, Vol.79, p.227-232

2013

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Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

IEEE transactions on electron devices, 2013-07, Vol.60 (7), p.2402-2409

2013

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An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

Advanced functional materials, 2010-01, Vol.20 (2), p.330-337

2010

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A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

Advanced functional materials, 2012-02, Vol.22 (3), p.609-616

2012

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Emerging resistive memories for low power embedded applications and neuromorphic systems

2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015, p.3088-3091

2015

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Visual Pattern Extraction Using Energy-Efficient "2-PCM Synapse" Neuromorphic Architecture

IEEE transactions on electron devices, 2012-08, Vol.59 (8), p.2206-2214

2012

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Physical aspects of low power synapses based on phase change memory devices

Journal of applied physics, 2012-09, Vol.112 (5)

2012

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Plasticity in memristive devices for spiking neural networks

Frontiers in neuroscience, 2015-03, Vol.9, p.51-51

2015

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Gold nanoparticle-pentacene memory transistors

Applied physics letters, 2008-03, Vol.92 (10), p.103314-103314-3

2008

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Two-Terminal Carbon Nanotube Programmable Devices for Adaptive Architectures

Advanced materials (Weinheim), 2010-02, Vol.22 (6), p.702-706

2010

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Development of a functional model for the Nanoparticle-Organic Memory transistor

2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2010, p.1663-1666

2010

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Spin-transfer torque magnetic memory as a stochastic memristive synapse

2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014, p.1074-1077

2014

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Simulation of a memristor-based spiking neural network immune to device variations

The 2011 International Joint Conference on Neural Networks, 2011, p.1775-1781

2011

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Addition of HfO$_2$ interface layer for improved synaptic performance of phase change memory (PCM) devices

Solid-state electronics, 2013, Vol.79, p.227-232

2013

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Corrigendum on 'Nanotube devices based crossbar architecture: toward neuromorphic computing'

Nanotechnology, 2010-06, Vol.21 (25), p.259801-259801

2010

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Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction

2011 International Electron Devices Meeting, 2011, p.4.4.1-4.4.4

2011

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On the impact of OxRAM-based synapses variability on convolutional neural networks performance

Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH´15), 2015, p.193-198

2015

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Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices

Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices, 2017, Vol.31, p.197-210

2017

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CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: Auditory (Cochlea) and visual (Retina) cognitive processing applications

2012 International Electron Devices Meeting, 2012, p.10.3.1-10.3.4

2012

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Challenges and Perspectives of Computer Architecture at the Nano Scale

2010 IEEE Computer Society Annual Symposium on VLSI, 2010, p.8-10

Gamrat, C

2010

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Learning with memristive devices: How should we model their behavior?

2011 IEEE/ACM International Symposium on Nanoscale Architectures, 2011, p.150-156

2011

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Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

2014 IEEE International Electron Devices Meeting, 2014, p.28.4.1-28.4.4

2014

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Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits

2015 IEEE International Electron Devices Meeting (IEDM), 2015, p.4.5.1-4.5.7

2015

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Nanotube devices based crossbar architecture: toward neuromorphic computing

Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

Addition of HfO2 interface layer for improved synaptic performance of phase change memory (PCM) devices

Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

Emerging resistive memories for low power embedded applications and neuromorphic systems

Visual Pattern Extraction Using Energy-Efficient "2-PCM Synapse" Neuromorphic Architecture

Physical aspects of low power synapses based on phase change memory devices

Plasticity in memristive devices for spiking neural networks

Gold nanoparticle-pentacene memory transistors

Two-Terminal Carbon Nanotube Programmable Devices for Adaptive Architectures

Development of a functional model for the Nanoparticle-Organic Memory transistor

Spin-transfer torque magnetic memory as a stochastic memristive synapse

Simulation of a memristor-based spiking neural network immune to device variations

Addition of HfO$_2$ interface layer for improved synaptic performance of phase change memory (PCM) devices

Corrigendum on 'Nanotube devices based crossbar architecture: toward neuromorphic computing'

Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction

On the impact of OxRAM-based synapses variability on convolutional neural networks performance

Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices

CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: Auditory (Cochlea) and visual (Retina) cognitive processing applications

Challenges and Perspectives of Computer Architecture at the Nano Scale

Learning with memristive devices: How should we model their behavior?

Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits

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