The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Nonlinear Dynamics of Memristor Oscillators
Abstract
A thorough investigation of the nonlinear dynamics of networks of memristor oscillators is a key step towards the design of systems based upon them, such as neuromorphic circuits and dense nonvolatile memories. A wide gamut of complex dynamic behaviors, including chaos, is observed even in a simple network of memristor oscillators, proposed here as a good candidate for the realization of oscillatory associative and dynamic memories. A detailed study of number and stability of all periodic and nonperiodic oscillations appearing in the network may not leave aside a preliminary deep understanding of the local and global behavior of the basic oscillator. Depending on two bifurcation parameters, controlling memristor nonlinearity, the oscillator exhibits different dynamic behaviors, analyzed here through application of state-of-the-art techniques from the theory of nonlinear dynamics to the oscillator model.
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Citations
Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 5×
“Finally, assuming that the order of complexity of graph is equal to (see Theorem 5 in [1] for details),defining the system state variables as capacitor fluxes, inductor charges and memristor flux, the nonredundant state equations (in other words the mathematical model) of the memristor oscillator associated with the g…”
BackgroundMethodInfluential(1998) · doi:10.1142/3481 · Cited by 4,436
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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“Conversely, parallel information processing of biological systems is supported in novel fully connected cell networks, where a unique synapse, directly coupling each pair of complementary metal-oxide semiconductor (CMOS) neurons, is implemented at each cross-point in a nanowire crossbar array lying above the neurons […”
BackgroundMemristive switching mechanism for metal/oxide/metal nanodevices
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Memristive devices and systems
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“A broader class of dynamic systems, termed memristive systems and including the memristor, were introduced by Kang and Chua in 1976 [2].”
Background‘Memristive’ switches enable ‘stateful’ logic operations via material implication
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Complementary resistive switches for passive nanocrossbar memories
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Experimental demonstration of associative memory with memristive neural networks
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Cited by
Chen, J., Zeng, Z., & Jiang, P. (2014) · Neural Networks · doi:10.1016/j.neunet.2013.11.016 · Cited by 554
Synchronization control of a class of memristor-based recurrent neural networks
Wu, A., Wen, S., & Zeng, Z. (2012) · Information Sciences · doi:10.1016/j.ins.2011.07.044 · Cited by 384
Adaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Wang, L., Shen, Y., Yin, Q., & Zhang, G. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2361776 · Cited by 200
Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays
Wu, A., & Zeng, Z. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.08.009 · Cited by 185
An Advection-Diffusion Model for the Vacancy Migration Memristor
Abraham, I. (2016) · IEEE Access · doi:10.1109/access.2016.2621721 · Cited by 8
Metadata
- Authors
- Corinto, F., Ascoli, A., & Gilli, M.
- Venue
- IEEE Transactions on Circuits and Systems I: Regular Papers
- Published
- 2011-06
- Volume
- 58
- Issue
- 6
- Pages
- 1323-1336
- ISSN
- 1549-8328 (ISSN-L)
- 1558-0806
- OpenAlex
- W2102216069
- Cited by
- 324
- References
- 21
- FWCI
- 17.60
- Type
- journal article
- Language
- en
- Open access
- closed
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