CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015)

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Accession:186768
This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition.
Reference:
1 . Sanjay M, Neymotin SA, Krothapalli SB (2015) Impaired dendritic inhibition leads to epileptic activity in a computer model of CA3. Hippocampus 25:1336-50 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA3 pyramidal cell; Hippocampus CA3 basket cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s): HCN1; HCN2;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Activity Patterns; Oscillations; Pathophysiology; Epilepsy; Brain Rhythms;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Sanjay, M [msanjaycmc at gmail.com];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal cell; Hippocampus CA3 basket cell; GabaA; AMPA; NMDA;
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SanjayEtAl2015
readme.html
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Epileptic Activity.png
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xtmp
                            
: $Id: MyExp2SynAlpha.mod,v 1.3 2010/12/13 21:36:40 samn Exp $ 
COMMENT
Two state kinetic scheme synapse described by rise time tau1,
and decay time constant tau2. The normalized peak condunductance is 1.
Decay time MUST be greater than rise time.

The solution of A->G->bath with rate constants 1/tau1 and 1/tau2 is
 A = a*exp(-t/tau1) and
 G = a*tau2/(tau2-tau1)*(-exp(-t/tau1) + exp(-t/tau2))
	where tau1 < tau2

If tau2-tau1 -> 0 then we have a alphasynapse.
and if tau1 -> 0 then we have just single exponential decay.

The factor is evaluated in the
initial block such that an event of weight 1 generates a
peak conductance of 1.

Because the solution is a sum of exponentials, the
coupled equations can be solved as a pair of independent equations
by the more efficient cnexp method.

ENDCOMMENT

NEURON {
	POINT_PROCESS MyExp2SynAlpha : alphasynapses
        RANGE tau1, tau2, e, i, g
	NONSPECIFIC_CURRENT i
}

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

PARAMETER {
	tau1=.1 (ms) <1e-9,1e9>
	tau2 = 10 (ms) <1e-9,1e9>
	e=0	(mV)
}

ASSIGNED {
	v (mV)
	i (nA)
	g (uS)
	factor
}

STATE {
	A (uS)
	B (uS)
}

INITIAL {
	LOCAL tp
	if (tau1/tau2 > .9999) {
		tau1 = .9999*tau2
	}
	A = 0
	B = 0
	tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
	factor = -exp(-tp/tau1) + exp(-tp/tau2)
	factor = 1/factor
}

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

DERIVATIVE state {
	A' = -A/tau1
	B' = -B/tau2
}

NET_RECEIVE(srcgid, weight (uS)) {
	A = A + weight*factor
	B = B + weight*factor
}

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