This project investigates the differences between two spiking neural network (SNN) models of the cerebellum. Using the NEST simulator, we implement a biologically inspired Leaky Integrate-and-Fire (LIF) network and compare its dynamics with a more detailed model that incorporates spatial information. The simplest way to inspect the code and its functioning is through Colab: check below for the link.
If you want to open and run this project with colab, follow this link: Colab Link
The goal of this project is to examine how different levels of abstraction in neural modeling influence network behavior. Specifically, we:
- Implement a Spiking Neural Network (SNN) in NEST:
- Construct a network of excitatory and inhibitory LIF neurons.
- Define synaptic connectivity and external inputs.
- Analyze neural activity using spike trains, raster plots, and average firing rates.
- Compare Network Dynamics:
- Measure firing rates across models.
- Examine temporal patterns, including oscillations and fluctuations.
- Compare some variables extracted from the results (e.g. Correlation coefficients)
To set up the environment, it is recommended to use Conda and install dependencies from the requirements.txt file.
Software & Libraries:
- NEST Simulator for SNN simulations.