This is a collection of tutorials showing how to use the NEST Simulator. You can find a detailed description for each tutorial in the following blog posts:
- NEST simulator – A powerful tool for simulating large-scale spiking neural networks
- Step-by-step NEST single neuron simulation
- Connection concepts in NEST
- Izhikevich SNN simulated with NEST
- Oscillatory population dynamics of GIF neurons simulated with NEST
- Brunel network: A comprehensive framework for studying neural network dynamics
- Example of a neuron driven by an inhibitory and excitatory neuron population
- What are alpha-shaped post-synaptic currents?
- Frequency-current (f-I) curves
- Olfactory processing via spike-time based computation
- Exponential (EIF) and adaptive exponential Integrate-and-Fire (AdEx) model
- Campbell and Siegert approximation for estimating the firing rate of a neuron
- Bienenstock-Cooper-Munro (BCM) rule
- On the role of gap junctions
- On the role of gap junctions in neural modelling: Network example
- Rate models as a tool for studying collective neural activity
For reproducibility:
conda create -n nest -y python=3.11 mamba
conda activate nest
mamba install -y ipykernel matplotlib numpy pandas nest-simulator