Waveform-relaxation methods for ordinary and stochastic differential equations with applications in distributed neural network simulations / eingereicht von Jan Hahne, M. Sc. Wuppertal, 29.05.2018
Inhalt
- Acknowledgments
- Foreword
- Contents
- Introduction
- Review of basic material
- Waveform-relaxation methods for ODEs
- Waveform-relaxation methods for SDEs
- Application in computational neuroscience I: Including gap junctions in a spiking neural network simulator
- Framework
- Algorithmic and numerical implementation
- Connection infrastructure
- Communication infrastructure
- Iterative neuronal updates
- Neuron model
- User interface
- Numerical results
- Setup
- Pair of gap-junction coupled neurons
- Network with combined dynamics of chemical synapses and gap junctions
- Performance of the gap-junction framework in NEST
- Discussion
- Application in computational neuroscience II: Including rate models in a spiking neural network simulator
- Conclusions & Outlook
- List of Figures
- List of Tables
- List of Algorithms & Scripts
- List of Notations
- Bibliography