GitHub - jiaangyao/CBG_network_sim: Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)

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Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)

modeldb.yale.edu/262046

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Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
Updated_PyNN_Files		Updated_PyNN_Files
matlab_analysis		matlab_analysis
python_analysis		python_analysis
.gitignore		.gitignore
Controllers.py		Controllers.py
CorticalAxonInterneuron_Connections.txt		CorticalAxonInterneuron_Connections.txt
CorticalSTN_Connections.txt		CorticalSTN_Connections.txt
CorticalSomaThalamic_Connections.txt		CorticalSomaThalamic_Connections.txt
Cortical_Axon_I_Kd.mod		Cortical_Axon_I_Kd.mod
Cortical_Axon_I_Kv.mod		Cortical_Axon_I_Kv.mod
Cortical_Axon_I_Leak.mod		Cortical_Axon_I_Leak.mod
Cortical_Axon_I_Na.mod		Cortical_Axon_I_Na.mod
Cortical_Basal_Ganglia_Cell_Classes.py		Cortical_Basal_Ganglia_Cell_Classes.py
Cortical_Soma_I_K.mod		Cortical_Soma_I_K.mod
Cortical_Soma_I_Leak.mod		Cortical_Soma_I_Leak.mod
Cortical_Soma_I_M.mod		Cortical_Soma_I_M.mod
Cortical_Soma_I_Na.mod		Cortical_Soma_I_Na.mod
Destexhe_Static_AMPA_Synapse.mod		Destexhe_Static_AMPA_Synapse.mod
Destexhe_Static_GABAA_Synapse.mod		Destexhe_Static_GABAA_Synapse.mod
Electrode_Distances.py		Electrode_Distances.py
GPeGPe_Connections.txt		GPeGPe_Connections.txt
GPeGPi_Connections.txt		GPeGPi_Connections.txt
GPeSTN_Connections.txt		GPeSTN_Connections.txt
GPe_Stimulation_Order.txt		GPe_Stimulation_Order.txt
GPiThalamic_Connections.txt		GPiThalamic_Connections.txt
Global_Variables.py		Global_Variables.py
InterneuronCortical_Connections.txt		InterneuronCortical_Connections.txt
Interneuron_I_K.mod		Interneuron_I_K.mod
Interneuron_I_Leak.mod		Interneuron_I_Leak.mod
Interneuron_I_Na.mod		Interneuron_I_Na.mod
STNGPe_Connections.txt		STNGPe_Connections.txt
STNGPi_Connections.txt		STNGPi_Connections.txt
STN_xy_pos.txt		STN_xy_pos.txt
StriatalGPe_Connections.txt		StriatalGPe_Connections.txt
Striatal_Spike_Times.npy		Striatal_Spike_Times.npy
SynNoise.mod		SynNoise.mod
ThalamicCorticalSoma_Connections.txt		ThalamicCorticalSoma_Connections.txt
Thalamic_I_Na_K.mod		Thalamic_I_Na_K.mod
Thalamic_I_T.mod		Thalamic_I_T.mod
Thalamic_I_leak.mod		Thalamic_I_leak.mod
burst_level_1.txt		burst_level_1.txt
burst_level_10.txt		burst_level_10.txt
burst_level_2.txt		burst_level_2.txt
burst_level_3.txt		burst_level_3.txt
burst_level_4.txt		burst_level_4.txt
burst_level_5.txt		burst_level_5.txt
burst_level_6.txt		burst_level_6.txt
burst_level_7.txt		burst_level_7.txt
burst_level_8.txt		burst_level_8.txt
burst_level_9.txt		burst_level_9.txt
burst_times_1.txt		burst_times_1.txt
burst_times_10.txt		burst_times_10.txt
burst_times_2.txt		burst_times_2.txt
burst_times_3.txt		burst_times_3.txt
burst_times_4.txt		burst_times_4.txt
burst_times_5.txt		burst_times_5.txt
burst_times_6.txt		burst_times_6.txt
burst_times_7.txt		burst_times_7.txt
burst_times_8.txt		burst_times_8.txt
burst_times_9.txt		burst_times_9.txt
cortical_collateral_electrode_distances.txt		cortical_collateral_electrode_distances.txt
cortical_xy_pos.txt		cortical_xy_pos.txt
myions.mod		myions.mod
pGPeA.mod		pGPeA.mod
pSTN.mod		pSTN.mod
readme.html		readme.html
run_CBG_Model_Amplitude_Modulation_Controller.py		run_CBG_Model_Amplitude_Modulation_Controller.py
run_CBG_Model_Frequency_Modulation_Controller.py		run_CBG_Model_Frequency_Modulation_Controller.py
run_CBG_Model_to_SS.py		run_CBG_Model_to_SS.py
xtra.mod		xtra.mod

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<html>
<p>Network model of the cortico-basal ganglia network with closed-loop DBS to test closed-loop DBS control strategies.
</p>
<p>This is the readme for the models associated with the paper:<p/>

  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<br/>
  <a href="http://dx.doi.org/10.3389/fnins.2020.00166">http://dx.doi.org/10.3389/fnins.2020.00166</a><p/>
</p>
<p>The model files were contributed by JE Fleming
</p>
<p>Model Requirements: - Model is simulated using PyNN with NEURON as
it's backend simulator, thus follow their installation instructions
  at:
<ol>
  <li> Neuron - <a href="https://www.neuron.yale.edu/neuron/download">https://www.neuron.yale.edu/neuron/download</a>
    </li><li> PyNN -
    <a href="https://pypi.org/project/PyNN/">https://pypi.org/project/PyNN/</a> - <a href="http://neuralensemble.org/docs/PyNN/">http://neuralensemble.org/docs/PyNN/</a>
    </li></ol>
</p>
<p>Model Setup:
</p>
<ol>
  <li>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.
  </li><li>Compile the NEURON model mod files using either mknrndll or
 nrnivmodl, for windows or Linux, respectively.
  </li><li>Run run_CBG_Model_to_SS.py
  <dl>
    <dt>Example</dt>
<dd>
  </dd></dl>
  </li><li>From the command line/terminal navigate to the folder containing
 the model.
  </li><li>Execute "python run_CBG_Model_to_SS.py neuron"
  <dl>
    <dt>Explanation</dt>
<dd>
  </dd></dl>
</li></ol>
<p>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)
</p>
<p>Subsequent runs of the model can use either
</p><p>    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.
</p>
<p>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:
</p>
<p>"python run_CBG_Model_Amplitude_Modulation_Controller.py neuron"
</p>
<p>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
<a href="https://neo.readthedocs.io/en/stable/.">https://neo.readthedocs.io/en/stable/.</a></p>
</html>