ArticleFrontiers in computational neuroscience2013
Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Vesicle and reaction-diffusion hybrid modeling with STEPS.Communications biology · 2024Article
- STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale.Frontiers in neuroinformatics · 2022Article
- Modeling Neurons in 3D at the Nanoscale.Advances in experimental medicine and biology · 2022Article
- Influence of T-Bar on Calcium Concentration Impacting Release Probability.Frontiers in computational neuroscience · 2022Article
- Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks.Frontiers in neuroinformatics · 2022Article
- Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers.Frontiers in neuroinformatics · 2017Article
- Python-based geometry preparation and simulation visualization toolkits for STEPS.Frontiers in neuroinformatics · 2014Article
- Stochastic calcium mechanisms cause dendritic calcium spike variability.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
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3 authors.
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Abstract
We describe a novel method for calculating the quasi-static electrical potential on tetrahedral meshes, which we call E-Field. The E-Field method is implemented in STEPS, which performs stochastic spatial reaction-diffusion computations in tetrahedral-based cellular geometry reconstructions. This provides a level of integration between electrical excitability and spatial molecular dynamics in realistic cellular morphology not previously achievable. Deterministic solutions are also possible. By performing the Rallpack tests we demonstrate the accuracy of the E-Field method. Efficient node ordering is an important practical consideration, and we find that a breadth-first search provides the best solutions, although principal axis ordering suffices for some geometries. We discuss potential applications and possible future directions, and predict that the E-Field implementation in STEPS will play an important role in the future of multiscale neural simulations.
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