Reference health
Perceptrons from memristors
36/36 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.
9 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 36 checked references that resolve
resolves10.1038/s41598-019-49204-y
Analog simulator of integro-differential equations with classical memristors
resolves10.1016/j.orgel.2015.06.015
Hardware elementary perceptron based on polyaniline memristive devices
resolves10.3115/v1/P14-1129
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
resolves10.1109/TNNLS.2014.2334701
Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications
resolves10.1063/1.4966257
First steps towards the realization of a double layer perceptron based on organic memristive devices
resolves10.1088/0957-4484/24/38/384010
Integration of nanoscale memristor synapses in neuromorphic computing architectures
resolves10.1016/j.neucom.2016.10.061
An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity
resolves10.1016/j.neunet.2010.05.001
Experimental demonstration of associative memory with memristive neural networks
resolves10.1109/JPROC.2011.2166369
Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements
resolves10.1038/nature14441
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
resolves10.1037/h0042519
The perceptron: A probabilistic model for information storage and organization in the brain.
resolves10.1109/TNNLS.2014.2383395
Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training
resolves10.1088/0957-4484/22/50/505402
Measuring the switching dynamics and energy efficiency of tantalum oxide memristors
resolves10.1016/S0950-5849(98)00116-5
Heuristic principles for the design of artificial neural networks
resolves10.1016/j.neunet.2018.03.015
General memristor with applications in multilayer neural networks
resolves10.1016/j.ins.2011.07.044
Synchronization control of a class of memristor-based recurrent neural networks
The 9 references without a DOI — listed, not checked
no DOI — not checkedMemristor-based perceptron classifier: Increasing complexity and coping with imperfect hardware
no DOI — not checkedGlorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249–256).
no DOI — not checkedEnabling back propagation training of memristor crossbar neuromorphic processors
no DOI — not checkedNonvolatile memristor memory: device characteristics and design implications
no DOI — not checked10.1016/j.neunet.2019.10.013_b22
no DOI — not checkedA review on memristive devices and applications
no DOI — not checkedExploring the design space of specialized multicore neural processors
no DOI — not checkedMemristive perceptron for combinational logic classification
no DOI — not checkedEnergy efficient perceptron pattern recognition using segmented memristor crossbar arrays
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work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
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