Functional properties of dendritic gap junctions in Cerebellar Golgi cells (Szoboszlay et al. 2016)

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Accession:189186
" ... We investigated the properties of gap junctions in cerebellar interneurons by combining paired somato-somatic and somato-dendritic recordings, anatomical reconstructions, immunohistochemistry, electron microscopy, and modeling. By fitting detailed compartmental models of Golgi cells to their somato-dendritic voltage responses, we determined their passive electrical properties and the mean gap junction conductance (0.9 nS). ..."
Reference:
1 . Szoboszlay M, Lorincz A, Lanore F, Vervaeke K, Silver RA, Nusser Z (2016) Functional Properties of Dendritic Gap Junctions in Cerebellar Golgi Cells. Neuron 90:1043-56 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum golgi cell;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; neuroConstruct;
Model Concept(s):
Implementer(s): Szoboszlay, M [szoboszlay.miklos at koki.mta.hu];
/
Golgi
01_Soma-dendritic_recordings
01_Control
140311-C1
syncytium
num3
gapCond.mod *
biophys.hoc *
cellCheck.hoc *
fitting.ses
fitting.ses.fd1 *
fitting.ses.ft1
init.hoc *
init_auto_fitting.hoc *
init_fitting.hoc *
morphology_140311_C1.hoc *
nCtools.hoc *
Passive_GoC_membrane_kinetics.hoc
processes.hoc *
                            
xopen("init_fitting.hoc")



objref Rm_vec, Cm_vec, Ra_vec, Ggj_vec
objref Inj_soma_error_vec, Vdend_error_vec, summa_inj_soma_error_vec

objref dataMatrix, dataFile

proc auto_fitting(){

	Rm_vec = new Vector()
	Cm_vec = new Vector()
	Ra_vec = new Vector()
	Ggj_vec = new Vector()
	
	Inj_soma_error_vec = new Vector()
	Vdend_error_vec = new Vector()
	summa_inj_soma_error_vec = new Vector()

	
	injSoma()
	MulRunFitter[0].prun()
	
	Rm_vec.append(user_Rm)
	Cm_vec.append(user_Cm)
	Ra_vec.append(user_Ra)
	Ggj_vec.append(gapWeight)
	Inj_soma_error_vec.append(MulRunFitter[0].p.pf.generatorlist.object(0).gen.efun())
	Vdend_error_vec.append(MulRunFitter[0].p.pf.generatorlist.object(1).gen.efun())
	summa_inj_soma_error_vec.append(MulRunFitter[0].p.pf.generatorlist.object(0).gen.efun() + MulRunFitter[0].p.pf.generatorlist.object(1).gen.efun())
	

	dataMatrix = new Matrix()
	
	dataMatrix.resize(Rm_vec.size(), 7)
	dataMatrix.setcol(0, Rm_vec)
	dataMatrix.setcol(1, Cm_vec)
	dataMatrix.setcol(2, Ra_vec)
	dataMatrix.setcol(3, Ggj_vec)
	dataMatrix.setcol(4, Inj_soma_error_vec)
	dataMatrix.setcol(5, Vdend_error_vec)
	dataMatrix.setcol(6, summa_inj_soma_error_vec)
	
	dataFile = new File()
	
	strdef dataFileName
	
	sprint(dataFileName, "FittingResults.dat")
	dataFile.wopen(dataFileName)
	dataMatrix.fprint(dataFile, " %g")
	dataFile.close()
	
	print "Finished, data saved"
}

gapWeight = 1000
auto_fitting()
quit()