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

No abstract is available for this paper.

• Publication year

  2011

• Venue

  IEEE/ACM International Symposium on Nanoscale Architectures

• Publication date

  2011-06-08

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/NANOARCH.2011.5941497
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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

  2011cited by this paper
• 2011 IEEE/ACM International Symposium on Nanoscale Architectures

  2011cited by this paper
• Investigating the switching dynamics and multilevel capability of bipolar metal oxide resistive switching memory

  2011cited by this paper
• Design and Modeling of a Neuro-Inspired Learning Circuit Using Nanotube-Based Memory Devices

  2011cited by this paper
• Hight fault tolerance in neural crossbar

  2010cited by this paper
• ‘Memristive’ switches enable ‘stateful’ logic operations via material implication

  2010cited by this paper
• Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions

  2010cited by this paper
• Nanoscale memristor device as synapse in neuromorphic systems.

  2010cited by this paper
• Nanotube devices based crossbar architecture: toward neuromorphic computing

  2010cited by this paper
• On neuromorphic spiking architectures for asynchronous STDP memristive systems

  2010influential reference
• Complementary resistive switches for passive nanocrossbar memories.

  2010cited by this paper
• The brain of a new machine

  2010cited by this paper
• A hybrid nanomemristor/transistor logic circuit capable of self-programming

  2009cited by this paper
• Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior.

  2009cited by this paper
• An electrically modifiable synapse array of resistive switching memory

  2009cited by this paper
• Switching dynamics in titanium dioxide memristive devices

  2009influential reference
• Memristor-CMOS hybrid integrated circuits for reconfigurable logic.

  2009cited by this paper
• Exponential ionic drift: fast switching and low volatility of thin-film memristors

  2009cited by this paper
• An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

  2009cited by this paper
• Electrical transport and thermometry of electroformed titanium dioxide memristive switches

  2009cited by this paper
• STDP enables spiking neurons to detect hidden causes of their inputs

  2009cited by this paper
• Four-dimensional address topology for circuits with stacked multilayer crossbar arrays

  2009cited by this paper
• The missing memristor found

  2008influential reference
• Programmable Resistance Switching in Nanoscale Two-terminal Devices

  2008cited by this paper
• Spike-timing-dependent learning in memristive nanodevices

  2008cited by this paper
• CMOS compatible nanoscale nonvolatile resistance switching memory.

  2008cited by this paper
• Memristive model of amoeba learning.

  2008cited by this paper
• Defect‐tolerant nanoelectronic pattern classifiers

  2007cited by this paper
• Self-organized computation with unreliable, memristive nanodevices

  2007influential reference
• Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

  2007cited by this paper
• Hybrid CMOS/nanoelectronic digital circuits: devices, architectures, and design automation

  2005cited by this paper
• CMOL FPGA: a reconfigurable architecture for hybrid digital circuits with two-terminal nanodevices

  2005cited by this paper
• Hybrid electronic device based on polyaniline-polyethyleneoxide junction

  2005cited by this paper
• Array-based architecture for FET-based, nanoscale electronics

  2003cited by this paper
• A Defect-Tolerant Computer Architecture: Opportunities for Nanotechnology

  1998cited by this paper
• Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs

  1997cited by this paper
• Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs

  1997cited by this paper
• Memristor-The missing circuit element

  1971cited by this paper
• Generalized Formula for the Electric Tunnel Effect between Similar Electrodes Separated by a Thin Insulating Film

  1963cited by this paper

• TraiNDSim: A Simulation Framework for Comprehensive Performance Evaluation of Neuromorphic Devices for On-Chip Training

  2024cites this paper
• Model of Neuromorphic Odorant-Recognition Network

  2023cites this paper
• Memristor-based spiking neural network with online reinforcement learning

  2023cites this paper
• Analog memristive devices based on La2NiO4+ δ as synapses for spiking neural networks

  2023cites this paper
• Efficient Hybrid Training Method for Neuromorphic Hardware Using Analog Nonvolatile Memory

  2023cites this paper
• CRUS: A Hardware-Efficient Algorithm Mitigating Highly Nonlinear Weight Update in CIM Crossbar Arrays for Artificial Neural Networks

  2022cites this paper
• Spoken Digits Classification Based on Spiking Neural Networks with Memristor-Based STDP

  2022cites this paper
• Analog synaptic devices applied to spiking neural networks for reinforcement learning applications

  2022cites this paper
• Novel Weight Update Scheme for Hardware Neural Network based on Synaptic Devices Having Abrupt LTP or LTD Characteristics

  2021cites this paper
• Development of a Short-Term to Long-Term Supervised Spiking Neural Network Processor

  2020cites this paper
• Improvement in conductance modulation linearity of artificial synapses based on NaNbO3 memristor

  2020cites this paper
• Memristor networks learn by mistakes

  2020cites this paper
• On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices

  2020cites this paper
• Adaptive Weight Quantization Method for Nonlinear Synaptic Devices

  2019cites this paper
• Studying on Improved Spiking Neural Network in Handwritten Digital Recognition

  2019cites this paper
• Contrasting Advantages of Learning With Random Weights and Backpropagation in Non-Volatile Memory Neural Networks

  2019cites this paper
• Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines

  2019cites this paper
• Spiking Neural Computing in Memristive Neuromorphic Platforms

  2019cites this paper
• Hardware-based Neural Networks using a Gated Schottky Diode as a Synapse Device

  2018cites this paper
• Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

  2018cites this paper
• Local learning with highly analog memory devices

  2018cites this paper
• Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices

  2017cites this paper
• A Survey of Neuromorphic Computing and Neural Networks in Hardware

  2017cites this paper
• N2S3, an Open-Source Scalable Spiking Neuromorphic Hardware Simulator

  2017cites this paper
• Unconventional computing with emerging RRAM nanodevices

  2016cites this paper
• Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator. (Calcul non conventionnel avec des nanocomposants memristifs : du calcul numérique auxaccélérateurs neuromorphiques)

  2016cites this paper
• Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing.

  2016cites this paper
• Combining a volatile and nonvolatile memristor in artificial synapse to improve learning in Spiking Neural Networks

  2016cites this paper
• Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits

  2015cites this paper
• Bioinspired Programming of Memory Devices for Implementing an Inference Engine

  2015influential citation
• Plasticity in memristive devices for spiking neural networks

  2015cites this paper
• Neuromorphic accelerators: A comparison between neuroscience and machine-learning approaches

  2015cites this paper
• On-Chip Universal Supervised Learning Methods for Neuro-Inspired Block of Memristive Nanodevices

  2015cites this paper
• Unsupervised learning of digit recognition using spike-timing-dependent plasticity

  2015cites this paper
• Robust learning approach for neuro-inspired nanoscale crossbar architecture

  2014cites this paper
• Spiking neuro-fuzzy clustering system and its memristor crossbar based implementation

  2014cites this paper
• Advanced technologies for brain-inspired computing

  2014cites this paper
• Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

  2013cites this paper
• Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

  2013cites this paper
• Technologies émergentes de mémoire résistive pour les systèmes et application neuromorphique

  2013cites this paper
• Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator

  2013cites this paper
• Physical aspects of low power synapses based on phase change memory devices

  2012cites this paper
• Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity

  2012cites this paper
• Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture

  2012cites this paper
• Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches

  2012cites this paper
• Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation

  2012cites this paper