This project is my final year project for the Department of Computer Science at the University of Manchester.
A Python GUI neuronal network simulator was implemented, and simulations were conducted to analyze different dynamical behaviors appearing in single neurons or neuronal networks and examine the synchronicity of the system.
- Spiking Neural Network (SNN)
- Brain-Inspired AI
- Computational Neuroscience
- Nonlinear System
- Dynamical System
- Python3
- Brian2 - Open-source Python Simulator for spiking neural networks
- Neurodynex - Python exercises for the book Neuronal Dynamics by Wulfram Gerstner, Werner M. Kistler, Richard Naud, and Liam Paninski
- PyDSTool - Simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems)
- PyQt5 - GUI widgets toolkit
Despite its size, a neuron is a very complicated entity; without sufficient biological and mathematical background knowledge, simulations of the neuronal network are out of reach. Therefore, the aim of the thesis is to implement networks of biologically accurate neuron models within computationally feasible cost and to simulate and analyze various electrophysiological behaviors including firing patterns of spiking neurons and the emergence of synchronization. As a result, several mathematical models of single neurons and networks of neurons were explored and simulated. In addition to this, the synchronization property of a network of neurons was analyzed in the presence and absence of the background stimuli.
| Regular Firing Patterns | Bursting Firing Patterns | Fast Firing Patterns |
|---|---|---|
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The research project report was published on the UoM departmental website and was selected as one of the best papers in 2018.
The report was written in LaTex via Overleaf, and the PDF version is included in this repository.