ArticlePLoS computational biology2016
Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function.Scientific reports · 2024Article
- Complex adaptive learning cortical neural network systems for solving time-fractional difference equations with bursting and mixed-mode oscillation behaviours.Scientific reports · 2023Article
- Diverse electrical responses in a network of fractional-order conductance-based excitable Morris-Lecar systems.Scientific reports · 2023Article
- Classification of bursting patterns: A tale of two ducks.PLoS computational biology · 2022Review
- Fractional-Order Traveling Wave Approximations for a Fractional-Order Neural Field Model.Frontiers in computational neuroscience · 2022Article
- Light/Clock Influences Membrane Potential Dynamics to Regulate Sleep States.Frontiers in neurology · 2021Review
- Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems.Chaos, solitons, and fractals · 2020Article
- Emergence of bursting in a network of memory dependent excitable and spiking leech-heart neurons.Journal of the Royal Society, Interface · 2020Article
- Firing activities of a fractional-order FitzHugh-Rinzel bursting neuron model and its coupled dynamics.Scientific reports · 2019Article
- Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow.Neuroinformatics · 2017Article
- SK channel subtypes enable parallel optimized coding of behaviorally relevant stimulus attributes: A review.Channels (Austin, Tex.) · 2017Review
- Automating NEURON Simulation Deployment in Cloud Resources.Neuroinformatics · 2017Article
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3 authors.
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Abstract
We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.
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