ArticleJournal of computational neuroscience2015
Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics.
Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Effect of Ion Channel Randomness on Sensitivity of Neurons to External Electromagnetic Fields: Computational Study.Entropy (Basel, Switzerland) · 2026Article
- Physical Frameworks and Theories on How Autonomous Biological Oscillators Synchronize.Advances in experimental medicine and biology · 2026Review
- Hippocampome.org, a resource for subicular neuron types and beyond.bioRxiv : the preprint server for biology · 2025Article
- Interleaving asynchronous and synchronous activity in balanced cortical networks with short term synaptic depression.Nature communications · 2025Article
- Efficient stochastic simulation of piecewise-deterministic Markov processes and its application to the Morris-Lecar model of neural dynamics.Biological cybernetics · 2025Article
- Mechanisms and physiological implications of cooperative gating of clustered ion channels.Physiological reviews · 2022Review
- Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators.Biological cybernetics · 2022Article
- DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks.PloS one · 2022Article
- Dynamical consequences of sensory feedback in a half-center oscillator coupled to a simple motor system.Biological cybernetics · 2021Article
- Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review.Bioelectricity · 2020Review
- Ion channel noise shapes the electrical activity of endocrine cells.PLoS computational biology · 2020Article
- Clusters of cooperative ion channels enable a membrane-potential-based mechanism for short-term memory.eLife · 2020Article
- Special Issue from the 2017 International Conference on Mathematical Neuroscience.Journal of mathematical neuroscience · 2019Article
- Stochastic Hybrid Systems in Cellular Neuroscience.Journal of mathematical neuroscience · 2018Review
- Stochastic shielding and edge importance for Markov chains with timescale separation.PLoS computational biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper we provide two representations for stochastic ion channel kinetics, and compare the performance of exact simulation with a commonly used numerical approximation strategy. The first representation we present is a random time change representation, popularized by Thomas Kurtz, with the second being analogous to a "Gillespie" representation. Exact stochastic algorithms are provided for the different representations, which are preferable to either (a) fixed time step or (b) piecewise constant propensity algorithms, which still appear in the literature. As examples, we provide versions of the exact algorithms for the Morris-Lecar conductance based model, and detail the error induced, both in a weak and a strong sense, by the use of approximate algorithms on this model. We include ready-to-use implementations of the random time change algorithm in both XPP and Matlab. Finally, through the consideration of parametric sensitivity analysis, we show how the representations presented here are useful in the development of further computational methods. The general representations and simulation strategies provided here are known in other parts of the sciences, but less so in the present setting.
Indexed as
Identifiers
25408289What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.