Metadata-Version: 2.4
Name: modelingneuraldynamics
Version: 1.0.0
Summary: Python and Brian2 code companion to 'An Introduction to Modeling Neuronal Dynamics' (Borgers), ported from the book's MATLAB originals
Author-email: Abolfazl Ziaeemehr <a.ziaeemehr@gmail.com>
License-Expression: GPL-3.0-or-later
Project-URL: Repository, https://github.com/ITNG/ModelingNeuralDynamics
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: networkx
Requires-Dist: brian2
Requires-Dist: jupyter
Requires-Dist: ipywidgets
Requires-Dist: ipykernel
Requires-Dist: numba
Dynamic: license-file

# ModelingNeuralDynamics
An Introduction to Modeling Neuronal Dynamics - Christoph Borgers in python
<p align="center">
<img src="https://raw.githubusercontent.com/ITNG/ModelingNeuralDynamics/main/python/30_The_PING_Model_of_Gamma_Rhythms/PING_4/fig.png"  width="600">
</p>
<p align="center">
 Ping Model of Gamma Rhythm 
</p>

<p align="center">
  <img src="https://raw.githubusercontent.com/ITNG/ModelingNeuralDynamics/main/python/22_A_Wilson_Cowan_Model_of_an_Oscillatory_E-I_Network/WILSON_COWAN_PHASE_PLANE/fig_22_3.png" width=300> 
</p>
<p align="center">
Wilson-Cowan phase plane
</p>

### Installation

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

```bash
pip install modelingneuraldynamics
```

### Running chapters on Colab

Every notebook under `brian/` can be opened directly in Google Colab —
click a chapter's badge below. The notebook installs its own
dependencies automatically when running on Colab.

| Chapter | Colab |
|---|---|
| 01 - Modeling a Single Neuron | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter01.ipynb) |
| 04 - Numerical Solution of HH ODEs | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter04.ipynb) |
| 05 - Three Simple Models of Neurons in Rodent Brains | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter05.ipynb) |
| 07 - Linear Integrate and Fire (LIF) Neurons | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter07.ipynb) |
| 08 - Quadratic Integrate and Fire (QIF) and Theta Neurons | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter08.ipynb) |
| 09 - Spike Frequency Adaptation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter09.ipynb) |
| 20 - Chemical Synapses | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ITNG/ModelingNeuralDynamics/blob/main/brian/chapter20.ipynb) |

### 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](https://link.springer.com/book/10.1007/978-3-319-51171-9)


### Python codes provided by contributors
See the [practical Python chapter guides](python/README.md) for the concepts,
equations, example map, and expected results for every implemented chapter.
