Network model of the parkinsonian motor network model including a cortico-basal ganglia network coupled to a motoneuron pool. The model simulates DBS to test tremor- and beta-based closed-loop DBS control strategies.
Model Requirements: - Model is simulated using PyNN with NEURON as it's backend simulator, thus follow their installation instructions at: 1) NEURON - https://nrn.readthedocs.io 2) PyNN - https://pypi.org/project/PyNN/ - http://neuralensemble.org/docs/PyNN/
Model Setup:
- Copy the included PyNN files from the downloaded model folder to their corresponding location on your computer (i.e. the directory of your PyNN instatllation - Updated PyNN files are needed for correct simulation of the multicompartmental cortical neurons and for loading model simulations from a presimulated steady state.
- Compile the NEURON model mod files using either
mknrndll
ornrnivmodl
, for windows or Linux, respectively. - Run
run_CBG_MU_Pool_Model_to_SS.py
Example: 1) From the command line/terminal navigate to the folder containing the model. 2) Execute "python run_CBG_Model_to_SS.py neuron
"
Explanation: There is an initial transient period in the model (~8 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 run_CBG_MU_Pool_Model_from_SS_Dual_Amplitude_Pulse_Duration_Modulation.py
to load the previously saved model steady state and run a
model simulation from this steady state point, and subsequently simulates closed-loop DBS.
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_MU_Pool_Model_from_SS_Dual_Amplitude_Pulse_Duration_Modulation.py neuron
Output files of the simulation are then written to a "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/.