Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells (Lee et al. 2014)

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Accession:153280
This detailed microcircuit model explores the network level effects of sublayer specific connectivity in the mouse CA1. The differences in strengths and numbers of synapses between PV+ basket cells and either superficial sublayer or deep sublayer pyramidal cells enables a routing of inhibition from superficial to deep pyramidal cells. At the network level of this model, the effects become quite prominent when one compares the effect on firing rates when either the deep or superficial pyramidal cells receive a selective increase in excitation.
Reference:
1 . Lee SH, Marchionni I, Bezaire M, Varga C, Danielson N, Lovett-Barron M, Losonczy A, Soltesz I (2014) Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells. Neuron 82:1129-44 [PubMed]
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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 CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s): I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; Glutamate;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Connectivity matrix; Laminar Connectivity;
Implementer(s): Bezaire, Marianne [mariannejcase at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; Glutamate; I Sodium; I Calcium; I Potassium;
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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 MyExp2Syn
	POINT_PROCESS MyExp2Sid
	RANGE tau1, tau2, e, i, sid, cid
	NONSPECIFIC_CURRENT i

	RANGE g
	GLOBAL total
}

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

PARAMETER {
	tau1=.1 (ms) <1e-9,1e9>
	tau2 = 10 (ms) <1e-9,1e9>
	e=0	(mV)
	sid = -1 (1) : synapse id, from cell template
	cid = -1 (1) : id of cell to which this synapse is attached
}

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

STATE {
	A (uS)
	B (uS)
}

INITIAL {
	LOCAL tp
	total = 0
	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(weight (uS)) {
	LOCAL srcid, w
	if (weight > 999) {
		srcid = floor(weight/1000) - 1
		w = weight - (srcid+1)*1000
	}else{
		w = weight
	}
	A = A + w*factor
	B = B + w*factor
	total = total+w
}