In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)

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Accession:258738
"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability"
Reference:
1 . Sherif MA, Neymotin SA, Lytton WW (2020) In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ Schizophr 6:25 [PubMed]
Citations  Citation Browser
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): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s): I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): NR2A GRIN2A;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Schizophrenia;
Implementer(s): Sherif, Mohamed [mohamed.sherif.md at gmail.com];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; AMPA; NMDA; I h; Gaba; Glutamate;
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CA3modelCode_npjSchizophrenia_September2020--main
data
README.md
CA1ih.mod
CA1ika.mod *
CA1ikdr.mod *
CA1ina.mod *
cagk.mod *
caolmw.mod *
capr.mod *
expsynstdp.mod
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HCN1.mod *
HCN2.mod
IA.mod
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ihstatic.mod *
infot.mod *
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km.mod
misc.mod *
MyExp2Syn.mod *
MyExp2SynAlpha.mod *
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MyExp2SynNMDA.mod *
MyExp2SynNMDABB.mod *
nafbwb.mod *
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samnutils.mod
sampen.mod
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analysisPlottingCode.py
aux_fun.inc *
batch.py
conf.py
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fig1sample.png
fig1simulationConfig.cfg
geom.py
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misc.h
network.py
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params.py
psd.py
pyinit.py
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run.py
runone.py
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updown.hoc
xgetargs.hoc *
                            
: $Id: expsynstdp.mod,v 1.3 2012/02/17 15:45:45 samn Exp $ 
:
: basic STDP exponential synapse
: from http://www.neuron.yale.edu/neuron/static/news/stdp.mod
:

NEURON {
	POINT_PROCESS ExpSynSTDP
	RANGE tau, e, i, d, p, dtau, ptau
	NONSPECIFIC_CURRENT i
        GLOBAL verbose
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	tau = 0.1 (ms) <1e-9,1e9>
	e = 0	(mV)
	d = 0 <0,1>: depression factor (multiplicative to prevent < 0)
	p = 0 : potentiation factor (additive, non-saturating)
	dtau = 34 (ms) : depression effectiveness time constant
	ptau = 17 (ms) : Bi & Poo (1998, 2001)
        verbose = 0 
}

ASSIGNED {
	v (mV)
	i (nA)
	tpost (ms)
}

STATE {
	g (uS)
}

INITIAL {
	g=0
	tpost = -1e9
	net_send(0, 1)
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	i = g*(v - e)
}

DERIVATIVE state {
	g' = -g/tau
}

NET_RECEIVE(w (uS), A, tpre (ms)) {
	INITIAL { A = 0  tpre = -1e9 }
	if (flag == 0) { : presynaptic spike  (after last post so depress)
          if(verbose) {printf("entry flag=%g t=%g w=%g A=%g tpre=%g tpost=%g\n", flag, t, w, A, tpre, tpost)}
		g = g + w*(1 + A)
		tpre = t
		A = A * (1 - d*exp((tpost - t)/dtau))
	}else if (flag == 2) { : postsynaptic spike
          if(verbose) {printf("entry flag=%g t=%g tpost=%g\n", flag, t, tpost)}
		tpost = t
		FOR_NETCONS(w1, A1, tp) { : also can hide NET_RECEIVE args
                  if(verbose) {printf("entry FOR_NETCONS w1=%g A1=%g tp=%g\n", w1, A1, tp)}
			A1 = A1 + p*exp((tp - t)/ptau)
		}
	} else { : flag == 1 from INITIAL block
          if(verbose) {printf("entry flag=%g t=%g\n", flag, t)}
		WATCH (v > -20) 2
	}
}