STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
about
Integration of biochemical and electrical signaling-multiscale model of the medium spiny neuron of the striatumApproaches and tools for modeling signaling pathways and calcium dynamics in neurons1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.Python as a federation tool for GENESIS 3.0A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation.STEPS: efficient simulation of stochastic reaction-diffusion models in realistic morphologiesPython-based geometry preparation and simulation visualization toolkits for STEPS.ViSimpl: Multi-View Visual Analysis of Brain Simulation Data.Trends in programming languages for neuroscience simulations.Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology.Effects of macromolecular crowding on intracellular diffusion from a single particle perspective.Comparison of models for IP3 receptor kinetics using stochastic simulations.Revealing neuronal function through microelectrode array recordingsReaction-diffusion in the NEURON simulator.Python in neuroscience.BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.Spatial simulations in systems biology: from molecules to cells.Single molecule simulations in complex geometries with embedded dynamic one-dimensional structuresReproducibility in Computational Neuroscience Models and Simulations.Postsynaptic signal transduction models for long-term potentiation and depression.Neuronize: a tool for building realistic neuronal cell morphologies.SBML and MathML support for the stochastic reaction-diffusion simulator STEPS.Improving performance of the STochastic Engine for Pathway Simulation (STEPS).Tetrahedral mesh generation and visualization for stochastic reaction-diffusion simulation.Computational Models for Calcium-Mediated Astrocyte Functions.Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.Spatially extended hybrid methods: a review.Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures.FindSim: A Framework for Integrating Neuronal Data and Signaling Models.Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics
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P2860
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
description
2009 nî lūn-bûn
@nan
2009 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@ast
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@en
type
label
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@ast
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@en
prefLabel
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@ast
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@en
P2860
P1476
STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.
@en
P2093
Stefan Wils
P2860
P356
10.3389/NEURO.11.015.2009
P577
2009-06-29T00:00:00Z