| STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies I Hepburn, W Chen, S Wils, E De Schutter BMC systems biology 6, 1-19, 2012 | 164 | 2012 |
| Stochastic calcium mechanisms cause dendritic calcium spike variability H Anwar, I Hepburn, H Nedelescu, W Chen, E De Schutter Journal of Neuroscience 33 (40), 15848-15867, 2013 | 45 | 2013 |
| The role of dendritic spine morphology in the compartmentalization and delivery of surface receptors CM Simon, I Hepburn, W Chen, E De Schutter Journal of computational neuroscience 36, 483-497, 2014 | 31 | 2014 |
| Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations I Hepburn, W Chen, E De Schutter The Journal of chemical physics 145 (5), 2016 | 13 | 2016 |
| Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS I Hepburn, R Cannon, E De Schutter Frontiers in computational neuroscience 7, 129, 2013 | 13 | 2013 |
| STEPS: STochastic Engine for Pathway Simulation I Hepburn Encyclopedia of Computational Neuroscience, 3309-3311, 2022 | 8 | 2022 |
| A model of induction of cerebellar long-term depression including RKIP inactivation of Raf and MEK I Hepburn, A Jain, H Gangal, Y Yamamoto, K Tanaka-Yamamoto, ... Frontiers in molecular neuroscience 10, 19, 2017 | 6 | 2017 |
| Modeling neurons in 3d at the nanoscale W Chen, I Hepburn, A Martyushev, E De Schutter Computational Modelling of the Brain: Modelling Approaches to Cells …, 2022 | 4 | 2022 |
| 26th annual computational neuroscience meeting (CNS* 2017): Part 3 AJH Newton, AH Seidenstein, RA McDougal, A Pérez-Cervera, G Huguet, ... BMC Neuroscience 18, 1-82, 2017 | 3 | 2017 |
| Accurate approximation and MPI parallelization of spatial stochastic reaction-diffusion in STEPS I Hepburn, W Chen, E De Schutter BMC Neuroscience 15 (Suppl 1), P177, 2014 | 3 | 2014 |
| Accurate approximation to stochastic reaction diffusion on unstructured meshes in STEPS I Hepburn, W Chen, E De Schutter BMC Neuroscience 16 (Suppl 1), P55, 2015 | 2 | 2015 |
| Improving performance of the STochastic Engine for Pathway Simulation (STEPS) W Chen, I Hepburn, E De Schutter BMC Neuroscience 12 (Suppl 1), P58, 2011 | 2 | 2011 |
| Tetrahedral mesh generation and visualization for stochastic reaction-diffusion simulation W Chen, I Hepburn, E De Schutter BMC Neuroscience 11 (Suppl 1), P70, 2010 | 2 | 2010 |
| STEPS: reaction-diffsion simulation in complex 3D geometries I Hepburn, S Wils, E De Schutter BMC Neuroscience 10 (Suppl 1), P51, 2009 | 2 | 2009 |
| STEPS.: Fast and memory-efficient molecular simulations of neurons at the nanoscale W Chen, T Carel, O Awile, N Cantarutti, G Castiglioni, A Cattabiani, ... Neuroscience, computing, performance, and benchmarks: Why it matters to …, 2023 | 1 | 2023 |
| Dynamic Regulation of Vesicle Pools in a Detailed Spatial Model of the Complete Synaptic Vesicle Cycle AR Gallimore, I Hepburn, S Rizzoli, E De Schutter bioRxiv, 2023.08. 03.551909, 2023 | 1 | 2023 |
| Dendritic Volume Mesh Reconstruction for STEPS: How Does Mesh Quality Affect Stochastic Reaction-Diffusion Simulation? W Chen, I Hepburn, E De Schutter BMC Neuroscience 14 (Suppl 1), P95, 2013 | 1 | 2013 |
| SBML and MathML support for the stochastic reaction-diffusion simulator STEPS I Hepburn, W Chen, E De Schutter BMC Neuroscience 12 (Suppl 1), P57, 2011 | 1 | 2011 |
| Reaction-Diffusion and MembranePotential Simulation with STEPS I Hepburn, W Chen, E De Schutter Neuroinformatics, 2010 | 1 | 2010 |
| Dynamic Regulation of Vesicle Pools in a Detailed Spatial Model of the Complete Synaptic Vesicle Cycle G AR, I Hepburn, S Rizzoli, E De Schutter | | 2023 |
