ArticleFrontiers in computational neuroscience2010
History-dependent Dynamics in a Generic Model of Ion Channels - an Analytic Study.
Article in Frontiers in computational neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Microcanonical coarse-graining of the kinetic Ising model.The Journal of chemical physics · 2020Article
- Dynamical Timescale Explains Marginal Stability in Excitability Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model.PLoS computational biology · 2014Article
- The neuronal response at extended timescales: long-term correlations without long-term memory.Frontiers in computational neuroscience · 2014Article
- Entrainment of the intrinsic dynamics of single isolated neurons by natural-like input.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
- Voltage-sensor movements describe slow inactivation of voltage-gated sodium channels I: wild-type skeletal muscle Na(V)1.4.The Journal of general physiology · 2013Article
- Conductance-based neuron models and the slow dynamics of excitability.Frontiers in computational neuroscience · 2012Article
- Stochastic differential equation models for ion channel noise in Hodgkin-Huxley neurons.Physical review. E, Statistical, nonlinear, and soft matter physics · 2011Article
- Dynamics of excitability over extended timescales in cultured cortical neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
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Authors and funding
2 authors.
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Abstract
Recent experiments have demonstrated that the timescale of adaptation of single neurons and ion channel populations to stimuli slows down as the length of stimulation increases; in fact, no upper bound on temporal timescales seems to exist in such systems. Furthermore, patch clamp experiments on single ion channels have hinted at the existence of large, mostly unobservable, inactivation state spaces within a single ion channel. This raises the question of the relation between this multitude of inactivation states and the observed behavior. In this work we propose a minimal model for ion channel dynamics which does not assume any specific structure of the inactivation state space. The model is simple enough to render an analytical study possible. This leads to a clear and concise explanation of the experimentally observed exponential history-dependent relaxation in sodium channels in a voltage clamp setting, and shows that their recovery rate from slow inactivation must be voltage dependent. Furthermore, we predict that history-dependent relaxation cannot be created by overly sparse spiking activity. While the model was created with ion channel populations in mind, its simplicity and genericalness render it a good starting point for modeling similar effects in other systems, and for scaling up to higher levels such as single neurons which are also known to exhibit multiple time scales.
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