Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)

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Accession:168314
This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network.
References:
1 . Hummos A, Franklin CC, Nair SS (2014) Intrinsic mechanisms stabilize encoding and retrieval circuits differentially in a hippocampal network model. Hippocampus 24:1430-48 [PubMed]
2 . Hummos A, Nair SS (2017) An integrative model of the intrinsic hippocampal theta rhythm. PLoS One 12:e0182648 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron; Abstract Izhikevich neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Acetylcholine; Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Epilepsy; Storage/recall;
Implementer(s):
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Acetylcholine; Gaba; Glutamate;
// 
// setting up the initial values for neuromodulators and sending commands to change these values at certain times for certain experiments 
Mods2 = ACHlevel

if (Experiment == 0 || Experiment == 1 || Experiment == 2) {
ACHe = 2//Mods2
ACHc = 2//Mods2

Plasticity = 1

ACH2 =Mods2
Da2 =Mods2

ACH =2
Da = 2

}

if (Experiment == 3 || Experiment == 4 || Experiment == 5) {
ACH = Mods2

LearningShutDown_pyr2pyr = 0
LearningShutDown_inter2pyr = 0
}

ACH_pyr2pyr = ACH
DA_pyr2pyr = Da
ACH_mossy = ACH
DA_mossy = Da
ACH_inter2pyr = ACH
ACH_IZH = ACH
DA_inter2pyr = Da
DA_IZH = Da

proc Commands() {
if (Experiment == 0) {
cvode.event(StimSpace * 5 , "LearningShutDown_pyr2pyr = 0")
cvode.event(StimSpace * 5 , "LearningShutDown_inter2pyr = 0")
cvode.event(StimSpace * 5 , "LearningShutDown_mossy = 0")

effectTime = StimSpace * 5

cvode.event(effectTime , "ACH_IZH = ACH2")
cvode.event(effectTime , "ACH_pyr2pyr = ACH2")
cvode.event(effectTime , "ACH_mossy = ACH2")
cvode.event(effectTime , "ACH_inter2pyr = ACH2")


cvode.event(effectTime , "DA_pyr2pyr = Da2")
cvode.event(effectTime , "DA_inter2pyr = Da2")
cvode.event(effectTime , "DA_mossy = Da2")

} else if (Experiment == 1) {
effectTime = (CondTrials) * TrialSpace
	cvode.event(effectTime , "ACH_IZH = 0")
	cvode.event(effectTime , "ACH_inter2pyr = 0")
	cvode.event(effectTime , "ACH_pyr2pyr = 0")
	cvode.event(effectTime , "ACH_pyr2emo = 0")
	cvode.event(effectTime , "ACH_mossy = 0")


effectTime = (CondTrials + EarlyTestTrials) * TrialSpace
	cvode.event(effectTime , "ACH_IZH = ACH2")
	cvode.event(effectTime , "ACH_inter2pyr = ACH2")
	cvode.event(effectTime , "ACH_pyr2pyr = ACH2")
	cvode.event(effectTime , "ACH_pyr2emo = ACH2")
	cvode.event(effectTime , "ACH_mossy = ACH2")

	if (Plasticity == 0) {
		cvode.event(effectTime , "LearningShutDown_pyr2pyr = 0")
		cvode.event(effectTime , "LearningShutDown_inter2pyr = 0")
	}

effectTime = (CondTrials + EarlyTestTrials + ExtTrials) * TrialSpace
	cvode.event(effectTime , "LearningShutDown_emo_pyr2pyr = 0")
	cvode.event(effectTime , "LearningShutDown_emo_inter2pyr = 0")

effectTime = (CondTrials + EarlyTestTrials + ExtTrials) * TrialSpace
		cvode.event(effectTime , "ACH_IZH = ACHe")
		cvode.event(effectTime , "ACH_inter2pyr = ACHe")
		cvode.event(effectTime , "ACH_pyr2pyr = ACHe")
		cvode.event(effectTime , "ACH_pyr2emo = ACHe")
		
effectTime = (CondTrials + EarlyTestTrials + ExtTrials + TestTrials) * TrialSpace
	
	cvode.event(effectTime , "ACH_IZH = ACHc")
	cvode.event(effectTime , "ACH_inter2pyr = ACHc")
	cvode.event(effectTime , "ACH_pyr2pyr = ACHc")
	cvode.event(effectTime , "ACH_pyr2emo = ACHc")
		

	} else if (Experiment == 2) {

cvode.event(StimSpace * 10 , "LearningShutDown_pyr2pyr = 0")
cvode.event(StimSpace * 10 , "LearningShutDown_inter2pyr = 0")
cvode.event(StimSpace * 10 , "LearningShutDown_mossy = 0")

effectTime = StimSpace * 10

cvode.event(effectTime , "ACH_IZH = ACH2")
cvode.event(effectTime , "ACH_pyr2pyr = ACH2")
cvode.event(effectTime , "ACH_inter2pyr = ACH2")
cvode.event(effectTime , "ACH_mossy = ACH2")
cvode.event(effectTime , "ACH_pyr2emo = ACH2")

cvode.event(effectTime , "DA_pyr2pyr = Da2")
cvode.event(effectTime , "DA_inter2pyr = Da2")
cvode.event(effectTime , "DA_mossy = Da2")
	}
}

// Setting the parameters simulation time step and duration
dttime  = .1 //.025
numsteps = 1/dttime

if (Experiment == 0 ) {

tstop = 10000
} else if(Experiment ==1) {

tstop = 12000
	if (ExtendedExt >0) {
		tstop = tstop + (12000/17 ) * ExtendedExt
	}

} else if (Experiment ==2) {

tstop = 13000
} else if (Experiment ==3) {

tstop = 15000
} else if (Experiment ==4) {

tstop = 400
} else if (Experiment ==5) {

tstop = 3000
}

{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[6]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}
{
xpanel("RunControl", 0)
v_init = -65
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = tstop
xvalue("t","t", 2 )
tstop = tstop
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = dttime //.025
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = numsteps //40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
screen_update_invl = 10 //0.05
xvalue("Scrn update invl","screen_update_invl", 1,"", 0, 1 )
realtime = 2.82
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(800,8) // 2000
}

	

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