The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Hidden Bursting Firings and Bifurcation Mechanisms in Memristive Neuron Model With Threshold Electromagnetic Induction
Abstract
Memristors can be employed to mimic biological neural synapses or to describe electromagnetic induction effects. To exhibit the threshold effect of electromagnetic induction, this paper presents a threshold flux-controlled memristor and examines its frequency-dependent pinched hysteresis loops. Using an electromagnetic induction current generated by the threshold memristor to replace the external current in 2-D Hindmarsh-Rose (HR) neuron model, a 3-D memristive HR (mHR) neuron model with global hidden oscillations is established and the corresponding numerical simulations are performed. It is found that due to no equilibrium point, the obtained mHR neuron model always operates in hidden bursting firing patterns, including coexisting hidden bursting firing patterns with bistability also. In addition, the model exhibits complex dynamics of the actual neuron electrical activities, which acts like the 3-D HR neuron model, indicating its feasibility. In particular, by constructing the fold and Hopf bifurcation sets of the fast-scale subsystem, the bifurcation mechanisms of hidden bursting firings are expounded. Finally, circuit experiments on hardware breadboards are deployed and the captured results well match with the numerical results, validating the physical mechanism of biological neuron and the reliability of electronic neuron.
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Cites
Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004764 · Cited by 23,191
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 4×
“Similar to the three classical circuit elements, the memristor is a link between the first-order electric and magnetic fields in the quasi-static expansion [1], whose nonlinear resistance or conductance can”
BackgroundMethodInfluentialTraining and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
NEURAL EXCITABILITY, SPIKING AND BURSTING
IZHIKEVICH, E. M. (2000) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127400000840 · Cited by 2,005
A model of neuronal bursting using three coupled first order differential equations
Hindmarsh, J. L., & Rose, R. M. (1984) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1984.0024 · Cited by 1,936
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide
Sangwan, V. K., Lee, H.-S., Bergeron, H., Balla, I., Beck, M. E., Chen, K.-S., & Hersam, M. C. (2018) · Nature · doi:10.1038/nature25747 · Cited by 1,023
A model of the nerve impulse using two first-order differential equations
Hindmarsh, J. L., & Rose, R. M. (1982) · Nature · doi:10.1038/296162a0 · Cited by 778
Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing
Kumar, S., Strachan, J. P., & Williams, R. S. (2017) · Nature · doi:10.1038/nature23307 · Cited by 617
Model of electrical activity in a neuron under magnetic flow effect
Lv, M., Wang, C., Ren, G., Ma, J., & Song, X. (2016) · Nonlinear Dynamics · doi:10.1007/s11071-016-2773-6 · Cited by 513
Cited by
Memristive Rulkov Neuron Model With Magnetic Induction Effects
Li, K., Bao, H., Li, H., Ma, J., Hua, Z., & Bao, B. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3086819 · Cited by 268
Self-citationDiscrete memristive neuron model and its interspike interval-encoded application in image encryption
Bao, H., Hua, Z., Liu, W., & Bao, B. (2021) · Science China Technological Sciences · doi:10.1007/s11431-021-1845-x · Cited by 85
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Metadata
- Authors
- Bao, H., Hu, A., Liu, W., & Bao, B.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2020-02
- Volume
- 31
- Issue
- 2
- Pages
- 502-511
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2972336707
- Cited by
- 371
- References
- 52
- FWCI
- 19.66
- Type
- journal article
- Language
- en
- Open access
- closed
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