Synaptic integration by MEC neurons (Justus et al. 2017)

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Accession:222359
Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes.
Reference:
1 . Justus D, Dalügge D, Bothe S, Fuhrmann F, Hannes C, Kaneko H, Friedrichs D, Sosulina L, Schwarz I, Elliott DA, Schoch S, Bradke F, Schwarz MK, Remy S (2017) Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections. Nat Neurosci 20:16-19 [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: Entorhinal cortex;
Cell Type(s): Entorhinal cortex pyramidal cell; Entorhinal cortex stellate cell; Entorhinal cortex fast-spiking interneuron;
Channel(s): I K; I Na,t; I h;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration; Simplified Models;
Implementer(s): Justus, Daniel [daniel.justus at dzne.de];
Search NeuronDB for information about:  AMPA; I Na,t; I K; I h; Glutamate;
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NEURON_mEC
data
README.html
exp2syn_depress.mod
h.mod *
kap.mod *
kdr.mod *
nax.mod
vecevent.mod *
cinit.hoc
EPSPparam.hoc
GUI.hoc
GUIfunctions.hoc
init.hoc
insert_noise_Syn.hoc
insert_real_Syn.hoc
insertsyn.hoc
morphology.hoc
mosinit.hoc *
Parameters.hoc
run_real_input.hoc
screenshot1.png
screenshot2.png
screenshot3.png
Voltage.ses
                            
objref SynR[10000]
objref NCR[10000]
objref NetSR[10000]
objref realStimtimes
objref sf

objref APc,APrec

nSyn=0


proc insert_real_Syn() {			// section,number,filename
	strdef unitdata
	sprint(unitdata,"data/%s",$s3)
	nSyn=0
	
	soma APc=new APCount(.5)
	APrec=new Vector()
	APc.thresh=-20
	APc.record(APrec)
	
	num=$2
	
	A=0
	forsec $o1{
		for (x) A+=area(x)
	}

	nSyn=0
	
	forsec $o1{		
		loc=0.5
		
		SynR[1]=new Exp2Syn_depress(loc)
		
		SynR[1].tau1=.01
		SynR[1].tau2=45
		SynR[1].tau_recover=300
		SynR[1].attenuation=0.5
		SynR[1].e=0	
	}
	
	sf=new File()
	sf.ropen(unitdata)

	realStimtimes=new Vector()
	realStimtimes.scanf(sf)
	sf.close()


	NetSR[1]=new VecStim()
	NetSR[1].play(realStimtimes)

	NCR[1]=new NetCon(NetSR[1],SynR[1])
	NCR[1].weight=num*9e-4
	NCR[1].delay=0
	cinit()
	
}

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