ModelingNeuralDynamics

An Introduction to Modeling Neuronal Dynamics - Christoph Borgers in python

tests PyPI Python versions License: GPL v3

PING gamma rhythm  ·  gamma coherence  ·  PING network with STDP

Installation

The shared helper package used by some chapters is on PyPI:

pip install modelingneuraldynamics

What's supported

Chapters are ported from the book's original MATLAB programs to Python. Every tracked chapter is now a single, tested chapterNN.ipynb notebook that installs its own dependencies and opens directly in Colab.

  • 38 / 38 tracked chapters converted to notebooks (✅)
  • Chapters 2 and 6 have no Python example in the book.
Full chapter-by-chapter status (click to expand)
# Chapter Status Open
1 Modeling a Single Neuron ✅ Notebook Open In Colab guide
3 The Classical HH ODEs ✅ Notebook Open In Colab guide
4 Numerical Solution of HH ODEs ✅ Notebook Open In Colab guide
5 The Simple Model of Neurons in Rodent Brains ✅ Notebook Open In Colab guide
7 Linear Integrate-and-Fire (LIF) Neurons ✅ Notebook Open In Colab guide
8 Quadratic Integrate-and-Fire (QIF) and Theta Neurons ✅ Notebook Open In Colab guide
9 Spike Frequency Adaptation ✅ Notebook Open In Colab guide
10 The Slow-Fast Phase Plane ✅ Notebook Open In Colab guide
11 The Saddle-Node Bifurcation ✅ Notebook Open In Colab guide
12 Two-Dimensional Bifurcation Analysis ✅ Notebook Open In Colab guide
13 Hopf Bifurcations ✅ Notebook Open In Colab guide
14 Model Neurons of Bifurcation Type 2 ✅ Notebook Open In Colab guide
15 Canard Explosions ✅ Notebook Open In Colab guide
16 Model Neurons of Bifurcation Type 3 ✅ Notebook Open In Colab guide
17 Frequency-Current Curves ✅ Notebook Open In Colab guide
18 Bistability Resulting from Rebound Firing ✅ Notebook Open In Colab guide
19 Bursting ✅ Notebook Open In Colab guide
20 Chemical Synapses ✅ Notebook Open In Colab guide
21 Gap Junctions ✅ Notebook Open In Colab guide
22 A Wilson-Cowan Model of an Oscillatory E-I Network ✅ Notebook Open In Colab guide
23 Entrainment by Excitatory Input Pulses ✅ Notebook Open In Colab guide
24 Synchronization by Fast Recurrent Excitation ✅ Notebook Open In Colab guide
25 Phase Response Curves (PRCs) ✅ Notebook Open In Colab guide
26 Phase Locking of Two Oscillators ✅ Notebook Open In Colab guide
27 Phase Locking with Delays ✅ Notebook Open In Colab guide
28 Weakly Coupled Oscillators ✅ Notebook Open In Colab guide
29 Stability of the Synchronous State ✅ Notebook Open In Colab guide
30 The PING Model of Gamma Rhythms ✅ Notebook Open In Colab guide
31 ING Rhythms ✅ Notebook Open In Colab guide
32 M-Current PING and Poisson PING ✅ Notebook Open In Colab guide
33 M-Current PING and PINB ✅ Notebook Open In Colab guide
34 Nested Gamma-Theta Rhythms ✅ Notebook Open In Colab guide
35 Periodic Inhibition ✅ Notebook Open In Colab guide
36 F-I Curves: Pulsed Excitation ✅ Notebook Open In Colab guide
37 Thresholding in PING ✅ Notebook Open In Colab guide
38 Gamma Coherence ✅ Notebook Open In Colab guide
39 Short-Term Depression and Facilitation ✅ Notebook Open In Colab guide
40 Spike-Timing-Dependent Plasticity (STDP) ✅ Notebook Open In Colab guide

Running brian/ chapters on Colab

brian/ holds a separate, Brian2-based implementation, one notebook per chapter. Every notebook there can also be opened directly in Google Colab — click a chapter's badge below.

Chapter Colab
01 - Modeling a Single Neuron Open In Colab
04 - Numerical Solution of HH ODEs Open In Colab
05 - Three Simple Models of Neurons in Rodent Brains Open In Colab
07 - Linear Integrate and Fire (LIF) Neurons Open In Colab
08 - Quadratic Integrate and Fire (QIF) and Theta Neurons Open In Colab
09 - Spike Frequency Adaptation Open In Colab
20 - Chemical Synapses Open In Colab

Introduction

This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology.

Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity.

An extensive online collection of Matlab programs generating the figures accompanies the book.

matlab code gathered from here

Python codes provided by contributors

See the practical Python chapter guides for the concepts, equations, example map, and expected results for every implemented chapter.

Project links

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Credits

Author: Abolfazl Ziaeemehr

GitHub Statistics

Data verified by PyPI on Aug 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

GPL-3.0-or-later
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Requires

Python >=3.10

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