This code belongs to the MSc Physics Thesis of Vishal Verma.
The EI-Spiking-Neural-Network-with-Delay simulates an excitatory inhibitory neural network with synaptic delays.
EI-Spiking-Neural-Network-with-Delay/
│── code/
│ ├── __init__.py
│ ├── snn_with_delays.py # (Main script)
│
│── tests/
│ ├── test.py # (test script)
│
│── README.md # (describes project)
│── requirements.txt # (Lists dependencies)
│── setup.py # (Making it an installable package)
│── docs # (documentation)
| │── INSTALLATION.md
| │── usage.md
|
|── LICENSE
- Simulation of neural network dynamics using delay differential equations
- Visualization tools for network activity
You can install the package directly from GitHub:
pip install git+https://github.com/im-vishalverma/EI-Spiking-Neural-Network-with-Delay.git
Here is a simple example of how to use the toolbox:
from code import snn_with_delays
num_neurons = 100
average_degree = 10
delay = 5
inh_fraction = 0.2
stimulated_neurons = 10
value = 0.07
num_sim = 1000
plot = 1
voltages, spikes = snn_with_delays.neural_network(
num_neurons, average_degree, delay, inh_fraction, stimulated_neurons, value, num_sim, plot
)
Example output from the toolbox for 1000 neurons:
To verify the installation and ensure everything is working correctly, run the test suite:
pytest tests/
We welcome contributions! To contribute, eg, consider including the effects of noise in the network
This project is licensed under the MIT License. See LICENSE
for details.
- For any inquiries or suggestions, reach out via GitHub Issues.
- Contributor: [email protected]
If you use this code in your research, please consider citing it:
@misc{SNNdelays,
author = {Vishal Verma},
title = {EI-Spiking-Neural-Network-with-Delay},
year = {2025},
url = {https://github.com/im-vishalverma/EI-Spiking-Neural-Network-with-Delay}
}