The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
NEURAL EXCITABILITY, SPIKING AND BURSTING
Abstract
Bifurcation mechanisms involved in the generation of action potentials (spikes) by neurons are reviewed here. We show how the type of bifurcation determines the neuro-computational properties of the cells. For example, when the rest state is near a saddle-node bifurcation, the cell can fire all-or-none spikes with an arbitrary low frequency, it has a well-defined threshold manifold, and it acts as an integrator; i.e. the higher the frequency of incoming pulses, the sooner it fires. In contrast, when the rest state is near an Andronov–Hopf bifurcation, the cell fires in a certain frequency range, its spikes are not all-or-none, it does not have a well-defined threshold manifold, it can fire in response to an inhibitory pulse, and it acts as a resonator; i.e. it responds preferentially to a certain (resonant) frequency of the input. Increasing the input frequency may actually delay or terminate its firing. We also describe the phenomenon of neural bursting, and we use geometric bifurcation theory to extend the existing classification of bursters, including many new types. We discuss how the type of burster defines its neuro-computational properties, and we show that different bursters can interact, synchronize and process information differently.
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Cites
Voltage oscillations in the barnacle giant muscle fiber
Morris, C., & Lecar, H. (1981) · Biophysical Journal · doi:10.1016/s0006-3495(81)84782-0 · Cited by 2,302
Llinás, R. R. (1988) · Science · doi:10.1126/science.3059497 · Cited by 2,285
Type I membranes, phase resetting curves, and synchrony
Ermentrout, B. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.979 · Cited by 1,001
Neuronal Networks of the Hippocampus
Traub, R. D., & Miles, R. (1991) · doi:10.1017/cbo9780511895401 · Cited by 802
Synchrony in excitatory neural networks
Hansel, D., Mato, G., & Meunier, C. (1995) · Neural Computation · doi:10.1162/neco.1995.7.2.307 · Cited by 587
Minimal model for membrane oscillations in the pancreatic beta-cell
Chay, T. R., & Keizer, J. (1983) · Biophysical Journal · doi:10.1016/s0006-3495(83)84384-7 · Cited by 482
Cited by
Simple model of spiking neurons
Izhikevich, E. M. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820440 · Cited by 4,828
Self-citationGrossberg, S. (2013) · Scholarpedia · doi:10.4249/scholarpedia.1888 · Cited by 602
Epileptic seizure prediction and control
Iasemidis, L. D. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810705 · Cited by 363
Tabak, J., Tomaiuolo, M., Gonzalez-Iglesias, A. E., Milescu, L. S., & Bertram, R. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3235-11.2011 · Cited by 63
Modelling cholesterol effects on the dynamics of the hypothalamic–pituitary–adrenal (HPA) axis
Marković, V. M., Čupić, Ž., Maćešić, S., Stanojević, A., Vukojević, V., & Kolar-Anić, L. (2016) · Mathematical Medicine and Biology · doi:10.1093/imammb/dqu020 · Cited by 31
Metadata
- Authors
- IZHIKEVICH, E. M.
- Venue
- International Journal of Bifurcation and Chaos
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2000-06
- Volume
- 10
- Issue
- 06
- Pages
- 1171-1266
- ISSN
- 0218-1274 (ISSN-L)
- 1793-6551
- OpenAlex
- W2119443761
- Cited by
- 2,005
- References
- 6
- FWCI
- 19.98
- Type
- journal article
- Language
- en
- Open access
- closed
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