daninci / snn_visual_cortex

Spiking Neural Network modelling the Visual Cortex (V1) Layer 5

Spiking Neural Network for the Visual Cortex (V1) Layer 5

This is a spike neural network model of the V1, layer 5 includes Cortico-Cortical (CC), Cortico-Subcortical (CS), PV and SST neurons.

Individual simulation metrics for neuron groups are computed such as firing rates, inter-spike intervals, burst detection.

Additionally, orientation and direction selectivity are aggregated for population of cells.

Project content

.
├── data                                        # Contains hypertuning results from amplitude simulation run
├── output                                      # Contains output results & plots from complete simulation run (.gitignored)
├── notebooks                                   # Jupyter notebooks used for testing different scenarios
│   ├── layer5_CC_CS_connection.ipynb           # Scalar plot simulation for weighted CC->CS connection probability
│   ├── layer5_SST_Soma_selectivity.ipynb       # Scalar plot simulation for weighted SST->CC/CS Soma connection probability
│   ├── layer5_sandbox.ipynb                    # Sandbox notebook used when creating initial network topology
│   └── ...               
├── layer5_CC_CS.py                             # Main network file that defines the topology of the simulation                   
├── equations.py                                # Equations for different neuron types
├── parameters.py                               # Default parameters for simulation
├── helpers.py                                  # Helper methods for analysis
├── plotting.py                                 # Helper methods for plotting
├── run_amplitude_hypertuing.py                 # Script for running multiple simulations for amplitude hypertuning for different neurons
└── run_complete_simulation.py                  # Complete simulation run entrypoint

Requirements

Use conda env create -n network -f requirements.txt to unpack the conda environment. Or download with conda install <module> the requirements manually.

After that, activate the environment with conda activate network

Hints

• There is only one best parameter set which I aggregated in parameters.py. If you want to try different combinations of parameters, you can change them here.
• To run main simulation python3 run_complete_simulation.py
• The project includes a script for hypertuning different input amplitudes for neuron groups. Can be run by python3 run_amplitude_hypertuning.py
• All simulation outputs are persisted in the output folder. I recommend deleting the content between successive runs to avoid confusion.
• Parallelization of simulations is missing. This would be a much needed improvement for the future.

Project Report

• For more details and documentation about the project scope, the report can be consulted.