Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)

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Accession:97972
Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).
References:
1 . Frank MJ (2006) Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw 19:1120-36 [PubMed]
2 . Frank MJ, Samanta J, Moustafa AA, Sherman SJ (2007) Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism. Science 318:1309-12 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell; Substantia nigra pars compacta DA cell; Subthalamus nucleus projection neuron; Globus pallidus neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: Emergent/PDP++;
Model Concept(s): Simplified Models; Synaptic Plasticity; Rate-coding model neurons; Parkinson's; Reinforcement Learning; Action Selection/Decision Making; Deep brain stimulation; Rebound firing;
Implementer(s): Frank, Michael [mfrank at u.arizona.edu];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; Substantia nigra pars compacta DA cell; Dopamine; Gaba; Glutamate;
#!/usr/local/bin/css -f

void Anal(String fnm) {
  fstream istrm;
  istrm.open(fnm, ios::in);

  fstream ostrm;
  ostrm.open(fnm + ".gort", ios::out);

  int epc;
  int rt_go[3200];
  int  cycle;
  int cycle_max = 200;
  float thal, m1;
  int i, l;
  int settle;
 int cycle_col, thal_col;

  while(!istrm.eof() && istrm.good()) {
    String_Array& col = ReadLine(istrm);
    if(istrm.eof() || istrm.bad()) break;

    if(col[0].contains("_H:")) {
      if (col[7].contains("cycle")) {
	cycle_col =7;  // columns for cycle and thalamus are different 
	thal_col = 12; // in c1 and c4 log files; adjust here
      }
      
      else {cycle_col = 8; thal_col = 13;}

      continue;
    }	


    cycle = col[cycle_col];
    thal = col[thal_col];
 
 if (cycle==0) {
   settle=0; 
   rt_go[i]= cycle_max; //initialize the rt to be maximum
 }	
    
 if (settle==0) { // check if net has settled on resp yet	
   if (thal>.25) { // check if average thal act is > .25, which means that a single unit > .5
     rt_go[i]=cycle; // if so, then set the rt to be this cycle
     settle =1;  // network has settled on response
   }
 }

 if (cycle== (cycle_max -1)) i++;
 
  }


  for (i=0;i<1600;i++) { 

    if ((rt_go[i]!=0)&&(rt_go[i]!=cycle_max))  
    ostrm << "_D:" << "\t" <<  i << "\t" << rt_go[i] << "\n";
  }  
  istrm.close();
  ostrm.close();
  cout << fnm << ".gort:\n";
  system("more " + fnm + ".gort");
  cout << "\n";
}

void Main() {
  int i;
  for(i=1;i<argc;i++) {
    Anal(argv[i]);
  }
}

Main();

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