Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware
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Catherine D. Schuman | Garrett S. Rose | Mst Shamim Ara Shawkat | Sagarvarma Sayyaparaju | Md Musabbir Adnan | Samuel D. Brown | G. Rose | Sagarvarma Sayyaparaju
[1] Garrett S. Rose,et al. Device-aware Circuit Design for Robust Memristive Neuromorphic Systems with STDP-based Learning , 2020, ACM J. Emerg. Technol. Comput. Syst..
[2] He Qian,et al. Unsupervised Learning on Resistive Memory Array Based Spiking Neural Networks , 2019, Front. Neurosci..
[3] Dan-Chun Hu,et al. Forming-free artificial synapses with Ag point contacts at interface , 2019, Journal of Materiomics.
[4] Edith Beigné,et al. Spiking Neural Networks Hardware Implementations and Challenges , 2019, ACM J. Emerg. Technol. Comput. Syst..
[5] Liang Fang,et al. Memristive Spiking Neural Networks Trained with Unsupervised STDP , 2018, Electronics.
[6] Zhigang Zeng,et al. Memristor-Based Circuit Design for Neuron With Homeostatic Plasticity , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.
[7] Catherine D. Schuman,et al. Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[8] Karsten Beckmann,et al. Design Considerations for Memristive Crossbar Physical Unclonable Functions , 2018, ACM J. Emerg. Technol. Comput. Syst..
[9] Mohammed A. Zidan,et al. Parasitic Effect Analysis in Memristor-Array-Based Neuromorphic Systems , 2018, IEEE Transactions on Nanotechnology.
[10] Yu Wang,et al. A Compact Memristor-Based Dynamic Synapse for Spiking Neural Networks , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[11] A S Spinelli,et al. Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity , 2017, Scientific Reports.
[12] Garrett S. Rose,et al. Robustness Analysis of a Memristive Crossbar PUF Against Modeling Attacks , 2017, IEEE Transactions on Nanotechnology.
[13] John Paul Strachan,et al. Oxygen migration during resistance switching and failure of hafnium oxide memristors , 2017, 1703.03106.
[14] Themis Prodromakis,et al. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning , 2016, Front. Neurosci..
[15] D. Jeong,et al. Memristors for Energy‐Efficient New Computing Paradigms , 2016 .
[16] Hassan Mostafa,et al. Statistical yield improvement under process variations of multi-valued memristor-based memories , 2016, Microelectron. J..
[17] N. Cady,et al. Nanoscale Hafnium Oxide RRAM Devices Exhibit Pulse Dependent Behavior and Multi-level Resistance Capability , 2016 .
[18] Matthew Cook,et al. Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..
[19] Vishal Saxena,et al. Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition , 2015, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[20] Vishal Saxena,et al. A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses and In Situ Learning , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.
[21] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[22] Deepak Khosla,et al. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.
[23] Hyunsang Hwang,et al. Neuromorphic Character Recognition System With Two PCMO Memristors as a Synapse , 2014, IEEE Transactions on Industrial Electronics.
[24] Shimeng Yu,et al. Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.
[25] Sylvain Saïghi,et al. Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).
[26] D. Querlioz,et al. Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices , 2013, IEEE Transactions on Nanotechnology.
[27] Leon O. Chua,et al. Memristor Bridge Synapse-Based Neural Network and Its Learning , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[28] Gert Cauwenberghs,et al. Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.
[29] Bernabé Linares-Barranco,et al. On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..
[30] J. Yang,et al. High switching endurance in TaOx memristive devices , 2010 .
[31] Timothée Masquelier,et al. Competitive STDP-Based Spike Pattern Learning , 2009, Neural Computation.
[32] Shih-Chii Liu,et al. Silicon synaptic adaptation mechanisms for homeostasis and contrast gain control , 2002, IEEE Trans. Neural Networks.
[33] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.
[34] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[35] L. Chua. Memristor-The missing circuit element , 1971 .