Network model of the cortico-basal ganglia network with closed-loop DBS to test closed-loop DBS control strategies.
This is the readme for the models associated with the paper:Fleming JE, Dunn E, Lowery MM (2020) Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson's Disease. Frontiers in Neuroscience 14:166
The model files were contributed by JE Fleming
Model Requirements: - Model is simulated using PyNN with NEURON as it's backend simulator, thus follow their installation instructions at:
There is an initial transient period in the model (~6 seconds). This model simulation runs the model for the transient period and creates a binary file (steady_state.bin) at the end of the simulation. This binary file captures the state of the model at the end of this transient simulation (i.e. after the model has reasched the steady state)
Subsequent runs of the model can use either
run_CBG_Model_Amplitude_Modulation_Controller.py or run_CBG_Model_Frequency_Modulation_Controller.py to load the previously saved model steady state and run a model simulation from this point simulating either amplitude or frequency modeulation, respectively.
Running the Model: - Once the steady state of the model has been saved you can run the model by navigating to the model directory in the command line and typing:
"python run_CBG_Model_Amplitude_Modulation_Controller.py neuron"
Output files of the simulation are then written to a "Simulation_Output_Results" folder when the simulation is finished. Model outputs are structured using the neo file format as detailed in https://neo.readthedocs.io/en/stable/.