The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Biophysical Basis for Three Distinct Dynamical Mechanisms of Action Potential Initiation
Open accessAbstract
Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding. Hodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. Despite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. We recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal model. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. Excitability could be converted between all three classes by varying single parameters; moreover, several parameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the spike-initiating dynamics associated with each of Hodgkin's classes represent different outcomes in a nonlinear competition between oppositely directed, kinetically mismatched currents. Class 1 excitability occurs through a saddle node on invariant circle bifurcation when net current at perithreshold potentials is inward (depolarizing) at steady state. Class 2 excitability occurs through a Hopf bifurcation when, despite net current being outward (hyperpolarizing) at steady state, spike initiation occurs because inward current activates faster than outward current. Class 3 excitability occurs through a quasi-separatrix crossing when fast-activating inward current overpowers slow-activating outward current during a stimulus transient, although slow-activating outward current dominates during constant stimulation. Experiments confirmed that different classes of spinal lamina I neurons express the subthreshold currents predicted by our simulations and, further, that those currents are necessary for the excitability in each cell class. Thus, our results demonstrate that all three classes of excitability arise from a continuum in the direction and magnitude of subthreshold currents. Through detailed analysis of the spike-initiating process, we have explained a fundamental link between biophysical properties and qualitative differences in how neurons encode sensory input.
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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
Impulses and Physiological States in Theoretical Models of Nerve Membrane
FitzHugh, R. (1961) · Biophysical Journal · doi:10.1016/s0006-3495(61)86902-6 · Cited by 6,258
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering
Strogatz, S., Friedman, M., Mallinckrodt, A. J., & McKay, S. (1994) · Computers in Physics · doi:10.1063/1.4823332 · Cited by 4,648
On the Theory of the Brownian Motion
Uhlenbeck, G. E., & Ornstein, L. S. (1930) · Physical Review · doi:10.1103/physrev.36.823 · Cited by 4,482
(1998) · doi:10.1142/3481 · Cited by 4,436
Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting
Izhikevich, E. M. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/2526.001.0001 · Cited by 3,765
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering
Strogatz, S. H., & Fox, R. F. (1995) · Physics Today · doi:10.1063/1.2807947 · Cited by 2,584
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
Intrinsic firing patterns of diverse neocortical neurons
Connors, B. W., & Gutnick, M. J. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90185-d · Cited by 1,448
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Hong, S., Ratté, S., Prescott, S. A., & De Schutter, E. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3735-11.2012 · Cited by 72
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Metadata
- Authors
- Prescott, S. A., De Koninck, Y., & Sejnowski, T. J.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-10-10
- Volume
- 4
- Issue
- 10
- Pages
- e1000198
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W1966337493
- Cited by
- 273
- References
- 61
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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