This is the readme for the model associated with the paper: Philip J. Hahn and Cameron C. McIntyre (2010). Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation. J. Comp. Neurosci. 28(3):425-441. This NEURON code (NEURON is required and available from http://www.neuron.yale.edu) contains a Basal Ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates. Instructions are provided here. Contact hahnp at ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading. These model files were supplied by Philip Hahn. 1. Contents readme.txt This file pSTN.tem STN neuron template pGPeA.tem GPe neuron template pGPi.tem GPi neuron template pSTN.mod STN kinetics pGPeA.mod GPe/GPi kinetics ampap.mod Glutamatergic synapse gabaap.mod Gabaergic synapse dbsStim.mod Stimulation induced current mechanism twoStateGen.mod Generate stochastic, bursting trains scopRandom.mod Hook scop_random RNG myions.mod Allow initialization of ion concentrations pBGconst.hoc Parameter handling support pBGbias.hoc Bias current support pBGconnect1.hoc Synaptic connections support pBGinput.hoc Support for generation of input spike trains pBGstim.hoc DBS stim current support pBGrecord.hoc Allow recording of overall GPi axonal output pBGutil.hoc General support pBGburstSearch.hoc Burst detection algorithm pBGconfData.hoc Simulation run script support parBGLaunch.hoc Main program file, launches simulation run scripts pBGLaunch.000 Sample simulation run script pPARrun.txt Default parameter settings (MPTP state) pNets.dat Published parameter set (best match) randseed.txt Random seed updated after each run 2. Running the model Either auto-launch the model from ModelDB and then explore the model with Tools -> modelview or hit the start button and wait about 15 seconds for two abreviated runs (1000 seconds of simulation time each) which should produce a network activity graph similar to Fig. 6b1 and Fig. 8b except the bursts are not identified: Or: First, recompile the mod files to generate a nrnmech.dll as needed for your system (eg mknrndll or nrnivmodl). Simulations are run by loading or executing parBGLaunch.hoc, which initializes the model and executes commands in a script file. The script file is named pBGLaunch.nnn, where 'nnn' is an integer entered by the user at the prompt. A sample script is included and described below (pBGLaunch.000) that runs a simulation in MPTP mode and then runs it again with DBS inputs active. For both runs population statistics as well as spike times are saved in text files. --------------- pBGLaunch.000 sample script file ------------------------------ strdef str tstop = 10000 //set the simulation time in milliseconds netNum = 0 getOutput("pNets.dat", netNum, 1) //load a parameter set setMPTPinput() //configure parameters for MPTP settings //setNORMALinput() //uncomment for Normal settings runNum = 0 pnm.prun() //run the model pnm.gatherspikes() //collect spikes from nodes (required for parallel runs) netStats(res) //calculate population statistics saveData(netNum, res) //append results to a file stats000.txt sprint(str, "spikes%03d.txt", runNum) //put identifying number in filename saveSpikes(str) //save spike time data for all cells to spikes000.txt runNum = 1 setDBS(.4,.2) //activate DBS for 40% of STN and 20% of GPi axons pnm.prun() //run the model pnm.gatherspikes() //collect spikes from nodes (required for parallel runs) netStats(res) //calculate population statistics saveData(netNum, res) //append results to a file stats000.txt sprint(str, "spikes%03d.txt", runNum) //put identifying number in filename saveSpikes(str) //save spike time data for all cells to spikes000.txt quit() //exit Neuron and close all windows ------------------------------------------------------------------------------- Note, omitting the last line will leave Neuron in the interactive mode when the simulation has completed. At that point, graph and run windows may be opened and the simulation rerun. 3. Platform notes Neuron v5.8 was used to run simulations. The included 'small net' has 50 cells (see pPARrun.txt) and runs well in a Windows XP environment. Published data used 500 cells in the same ratio of 1:3:1 (STN:GPe:GPi).Data collection was performed on a 16 node cluster using Neuron's network manager objects over MPI. This model is currently being ported to a Python version of Neuron.