Reference health
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses
50/50 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
13 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 50 checked references that resolve
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A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity
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Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model
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Self-organized computation with unreliable, memristive nanodevices
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Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
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Voltage-time dilemma of pure electronic mechanisms in resistive switching memory cells
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A Memristor SPICE Model Accounting for Synaptic Activity Dependence
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Simulation of a memristor-based spiking neural network immune to device variations
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STDP and STDP variations with memristors for spiking neuromorphic learning systems
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Neuromorphic Character Recognition System With Two PCMO Memristors as a Synapse
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Stochastic learning in oxide binary synaptic device for neuromorphic computing
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Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
resolves10.3389/fnins.2015.00357
Implementation of a spike-based perceptron learning rule using TiO2−x memristors
resolves10.1002/adma.201503202
Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity
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Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics
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Integration of nanoscale memristor synapses in neuromorphic computing architectures
resolves10.1038/nature14441
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
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Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
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Bioinspired Programming of Memory Devices for Implementing an Inference Engine
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A high-precision VLSI winner-take-all circuit for self-organizing neural networks
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A Current-Mode Hysteretic Winner-take-all Network, with Excitatory and Inhibitory Coupling
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CMOS current mode winner-take-all circuit with both excitatory and inhibitory feedback
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STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning
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A <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays
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High density 3D memory architecture based on the resistive switching effect
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Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
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Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains
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The 13 references without a DOI — listed, not checked
no DOI — not checkedIndiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).
no DOI — not checkedGovoreanu, B. et al. in Proc. 2011 IEEE International Electron Devices Meeting (ed. Mahoney, P. W.) 31.6 (IEEE, (2011).
no DOI — not checkedGupta, I. et al. Memristive integrative sensors for neuronal activity. Preprint at http://arxiv.org/abs/1507.06832 (2015).
no DOI — not checkedMa, X., Strukov, D. B., Lee, J. H. & Likharev, K. K. in Proc. 5th IEEE Conference on Nanotechnology (ed. Arai, F.) 175–178 (IEEE, (2005).
no DOI — not checkedWei, S. L. et al. Emulating long-term synaptic dynamics with memristive devices. Preprint at http://arxiv.org/abs/1509.01998 (2015).
no DOI — not checkedDu, N. et al. Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs. Front. Neurosci. 9, 227 (2015).
no DOI — not checkedMayr, C. et al. in Proc. NIPS 2012 - Advances in Neural Information Processing Systems 25 (ed. Pereira, F. et al.) 1700–1708 (Neural Information Processing Systems Foundation, Inc., 2012).
no DOI — not checkedBill, J. & Legenstein, R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Front. Neurosci. 8, 412 (2014).
no DOI — not checkedHabenschuss, S., Bill, J. & Nessler, B. in Proc. NIPS 2012 - Advances in Neural Information Processing Systems 25 (eds Pereira, F. et al.) 773–781 (Neural Information Processing Systems Foundation, Inc., (2012).
no DOI — not checkedNessler, B., Pfeiffer, M. & Maass, W. in Advances in Neural Information Processing Systems, 1357–1365 (2009).
no DOI — not checkedLeung, W., Hsu, F.-C. & Jones, M.-E. in Proc. IEEE International ASIC/SOC Conference (eds Buchner, T. et al.) 32–36 (IEEE, (2000).
no DOI — not checkedBurr, G. W. et al. in Proc. VLSIT 2013 - Symposium on VLSI Technology (ed. Jammy, R.) T66–T67 (IEEE, (2013).
no DOI — not checkedKappel, D., Habenschuss, S., Legenstein, R. & Maass, W. in Advances in Neural Information Processing Systems, 370–378 (2015).
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