Neuronal Dynamics: Python Exercises¶
This documentation is automatically generated documentation from the corresponding code repository hosted at Github. The repository contains python exercises accompanying the book Neuronal Dynamics by Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski.
Contents¶
- Introduction
- Exercises
- 1. Setting up Python and Brian
- 2. Leaky-integrate-and-fire model
- 3. The Exponential Integrate-and-Fire model
- 4. AdEx: the Adaptive Exponential Integrate-and-Fire model
- 5. Dendrites and the (passive) cable equation
- 6. Numerical integration of the HH model of the squid axon
- 7. FitzHugh-Nagumo: Phase plane and bifurcation analysis
- 8. Hopfield Network model of associative memory
- 9. Type I and type II neuron models
- 10. Oja’s hebbian learning rule
- 11. Network of LIF neurons (Brunel)
- 12. Spatial Working Memory (Compte et. al.)
- 13. Perceptual Decision Making (Wong & Wang)
- Python exercise modules
- neurodynex3 package
- Subpackages
- neurodynex3.adex_model package
- neurodynex3.brunel_model package
- neurodynex3.cable_equation package
- neurodynex3.competing_populations package
- neurodynex3.exponential_integrate_fire package
- neurodynex3.hodgkin_huxley package
- neurodynex3.hopfield_network package
- neurodynex3.leaky_integrate_and_fire package
- neurodynex3.neuron_type package
- neurodynex3.ojas_rule package
- neurodynex3.phase_plane_analysis package
- neurodynex3.test package
- neurodynex3.tools package
- neurodynex3.working_memory_network package
- Module contents
- Subpackages
- neurodynex3 package
- License