Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)

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This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1
1 . Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I (2016) Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. Elife [PubMed]
2 . Bezaire M, Raikov I, Burk K, Armstrong C, Soltesz I (2016) SimTracker tool and code template to design, manage and analyze neural network model simulations in parallel NEURON bioRxiv
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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 interneuron oriens alveus GABA cell; Hippocampus CA1 basket cell; Hippocampus CA1 stratum radiatum interneuron; Hippocampus CA1 bistratified cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA1 PV+ fast-firing interneuron;
Channel(s): I Na,t; I K; I K,leak; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; GabaB; Glutamate; Gaba;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; NEURON (web link to model);
Model Concept(s): Oscillations; Methods; Connectivity matrix; Laminar Connectivity; Gamma oscillations;
Implementer(s): Bezaire, Marianne [mariannejcase at gmail.com]; Raikov, Ivan [ivan.g.raikov at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; GabaA; GabaB; Glutamate; Gaba; I Na,t; I K; I K,leak; I h; I K,Ca; I Calcium; Gaba; Glutamate;
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.


	RANGE tau1a, tau2a, tau1b, tau2b, ea, eb, i, sid, cid

	RANGE ga, gb
	GLOBAL totala, totalb

	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)

	tau1a=.1 (ms) <1e-9,1e9>
	tau2a = 10 (ms) <1e-9,1e9>
	ea=0	(mV)
	tau1b=.1 (ms) <1e-9,1e9>
	tau2b = 10 (ms) <1e-9,1e9>
	eb=0	(mV)
	sid = -1 (1) : synapse id, from cell template
	cid = -1 (1) : id of cell to which this synapse is attached

	v (mV)
	i (nA)
	ga (uS)
	totala (uS)
	gb (uS)
	totalb (uS)

	Aa (uS)
	Ba (uS)
	Ab (uS)
	Bb (uS)

	LOCAL tpa, tpb
	totala = 0
	totalb = 0
	if (tau1a/tau2a > .9999) {
		tau1a = .9999*tau2a
	if (tau1b/tau2b > .9999) {
		tau1b = .9999*tau2b
	Aa = 0
	Ba = 0
	Ab = 0
	Bb = 0
	tpa = (tau1a*tau2a)/(tau2a - tau1a) * log(tau2a/tau1a)
	factora = -exp(-tpa/tau1a) + exp(-tpa/tau2a)
	factora = 1/factora
	tpb = (tau1b*tau2b)/(tau2b - tau1b) * log(tau2b/tau1b)
	factorb = -exp(-tpb/tau1b) + exp(-tpb/tau2b)
	factorb = 1/factorb

	SOLVE state METHOD cnexp
	ga = Ba - Aa
	gb = Bb - Ab
	i = ga*(v - ea) + gb*(v - eb)

	Aa' = -Aa/tau1a
	Ba' = -Ba/tau2a
	Ab' = -Ab/tau1b
	Bb' = -Bb/tau2b

NET_RECEIVE(weight (uS)) {
	LOCAL srcid, w
	if (weight > 999) {
		srcid = floor(weight/1000) - 1
		w = weight - (srcid+1)*1000
		w = weight
	Aa = Aa + w*factora
	Ba = Ba + w*factora
	totala = totala+w
	Ab = Ab + w*factorb/3.37
	Bb = Bb + w*factorb/3.37
	totalb = totalb+w/3.37