Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)

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Accession:112685
"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting."
References:
1 . Solinas S, Forti L, Cesana E, Mapelli J, De Schutter E, D'Angelo E (2007) Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar golgi cells. Front. Cell. Neurosci. 1:2:1-12 [PubMed]
2 . Solinas S, Forti L, Cesana E, Mapelli J, De Schutter E, D'Angelo E (2007) Fast-reset of pacemaking and theta-frequency resonance patterns in cerebellar golgi cells: Simulations of their impact in vivo. Front. Cell. Neurosci. 1:4:1-9 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum golgi cell;
Channel(s): I Na,p; I Na,t; I T low threshold; I A; I K; I M; I K,Ca; I Sodium; I Calcium; I Potassium; I h;
Gap Junctions:
Receptor(s):
Gene(s): HCN1;
Transmitter(s):
Simulation Environment: NEURON; neuroConstruct (web link to model);
Model Concept(s): Activity Patterns; Oscillations;
Implementer(s): D'Angelo, Egidio [dangelo at unipv.it]; De Schutter, Erik [erik at oist.jp];
Search NeuronDB for information about:  I Na,p; I Na,t; I T low threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Files displayed below are from the implementation
/
Golgi_cell
sessions
readme.html
Golgi_BK.mod *
Golgi_Ca_HVA.mod *
Golgi_Ca_LVA.mod *
Golgi_CALC.mod *
Golgi_CALC_ca2.mod *
Golgi_hcn1.mod *
Golgi_hcn2.mod *
Golgi_KA.mod *
Golgi_KM.mod *
Golgi_KV.mod *
Golgi_lkg.mod *
Golgi_Na.mod *
Golgi_NaP.mod *
Golgi_NaR.mod *
Golgi_SK2.mod *
Pregen.mod *
Synapse.mod *
Channel_dynamics.hoc *
Golgi_ComPanel.hoc *
Golgi_count.txt
Golgi_template.hoc
mosinit.hoc
Save_data.hoc *
screenshot.jpg
Start_golgi.hoc
Synapses.hoc *
utils.hoc *
                            
/*******Cerebellar Golgi Cell Model **********

Developers:    Sergio Solinas & Egidio D'Angelo
Code contributors:  Thierry Neius, Shyam Diwakar, Lia Forti
Data Analysis: Sergio Solinas

Work Progress: April 2004 - May 2007

Developed At:  Università Degli Studi Di Pavia
	       Dipartimento Di Scienze Fisiologiche
	       Pavia - Italia
	       
Model Published in: 
             Sergio M. Solinas, Lia Forti, Elisabetta Cesana, 
             Jonathan Mapelli, Erik De Schutter and Egidio D`Angelo (2008)
             Computational reconstruction of pacemaking and intrinsic 
             electroresponsiveness in cerebellar golgi cells
             Frontiers in Cellular Neuroscience 2:2


********************************************/

objref dta, dta2

dta = new Vector () 
dta2 = new Vector ()

proc FR() {
  
  print "spike times (ms): "
  $o1.printf()
  dta.deriv($o1,1,1)
  print "In terspike intervals (ms): "
  dta.printf()
  dta2.resize(dta.size())
  dta2.fill(1000)
  dta2.div(dta)
  print "Inst. Firing rates (sp/s): "
  dta2.printf()
  print "  first ISI\t  Last ISI\tAdaptation\tMean rate\tCV-ISI\n"
  printf("\t%-5.2g\t\t%-5.2g\t\t%-5.2g\t\t%-5.2g\t%-5.2g\n", dta2.x[0], dta2.x[dta2.size()-1],dta2.x[dta2.size()-1]/dta2.x[0], 1000/dta.mean(), dta.stdev()/dta.mean())
}

proc PrintRates() {
    print "Control\tIh\tKM\tSK-bSK2\tHVA\tKA"
    $o1.getcol(9).printf
    $o1.getcol(1).div($o1.getcol(1).get(0)/100).sub(100).printf
}

func Cell_area() { local surf
    surf = 0
    forall { surf = surf + area(0.5)*nseg}
    return surf
}

proc Cell_Pass() { local cnm
    cnm = 0
    forall { cnm = cnm + area(0.5)*nseg*glbar_Golgi_lkg*1e-8}
    print "Resistance\t",1/cnm*1e-6," MOhm"
    cnm = 0
    forall { cnm = cnm + area(0.5)*nseg*cm*1e-8}
    print "Capacitance\t",cnm*1e6," pF"
}

proc FR_show() {
	FR(Golgi[0].SpikeTrain)
    }
    


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