In this project, we conducted a comparative analysis of the FitzHugh-Nagumo (FHN) and Hindmarsh-Rose (HR) models to understand their effectiveness in modeling neuronal dynamics. Our work included simulating these models under various conditions, analyzing their responses to different stimuli, and evaluating their capabilities in replicating the complex behaviors observed in neurons. This practical approach allowed us to highlight the strengths and limitations of each model, offering insights into their applicability in computational neuroscience research.
This project utilizes Python 3.x, leveraging libraries such as NumPy for numerical computations, Matplotlib for visualization, and SciPy for advanced scientific computing.
- Clone the repository:
git clone https://github.com/PatrizioAcquadro/Neural-Models-Comparative-Analysis.git - Install the necessary dependencies:
pip install -r requirements.txt - Explore the Jupyter notebooks for an interactive analysis and comparison of the models.
- In-depth exploration and comparison of the FHN and HR models' ability to simulate neuronal dynamics.
- Detailed analysis of the models' responses to external stimuli, showcasing their strengths and limitations.
- Visualizations highlighting the complex behaviors captured by each model.
- Contributions to the field of computational neuroscience through a critical evaluation of model applicability.
We extend our gratitude to the academic and research community for their support and inspiration throughout this project.
- Patrizio Acquadro
- Mattia Moro
- Lorenzo Uttini