A single column thalamocortical network model (Traub et al 2005)

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To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary.
1 . Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FE, Roopun A, Bibbig A, Wilent WB, Higley MJ, Whittington MA (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93:2194-232 [PubMed]
2 . Traub RD, Contreras D, Whittington MA (2005) Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo. J Clin Neurophysiol 22:330-42 [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: Neocortex; Thalamus;
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Simulation Environment: NEURON; FORTRAN;
Model Concept(s): Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Simplified Models; Epilepsy; Sleep; Spindles;
Implementer(s): Traub, Roger D [rtraub at us.ibm.com];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Files displayed below are from the implementation
alphasyndiffeq.mod *
alphasynkin.mod *
alphasynkint.mod *
ampa.mod *
ar.mod *
cad.mod *
cal.mod *
cat.mod *
cat_a.mod *
gabaa.mod *
iclamp_const.mod *
k2.mod *
ka.mod *
ka_ib.mod *
kahp.mod *
kahp_deeppyr.mod *
kahp_slower.mod *
kc.mod *
kc_fast.mod *
kdr.mod *
kdr_fs.mod *
km.mod *
naf.mod *
naf_tcr.mod *
naf2.mod *
nap.mod *
napf.mod *
napf_spinstell.mod *
napf_tcr.mod *
par_ggap.mod *
pulsesyn.mod *
rampsyn.mod *
rand.mod *
ri.mod *
traub_nmda.mod *
 ampa.mod is 
 alphasyndiffeqt.mod which is actually
 exp2syn.mod (default supplied with NEURON) modified so that the
 time constants are very close to each other.  The new global
 near_unity_AlphaSynDiffEqT is the factor multiplied into
 tau2 to make tau1. 
 Note: that tau2 was renamed tau so that it would be obvious
 which time constant to set.
This program was then further modified to make
 more similar to Traub et al 2005:
delta = time-presyn
dexparg = delta/tau
if (dexparg <= 100
	z = exp(-dexparg)
	z = 0
g = g + g_0 * delta * z
and current = (g_ampa + open(i) * g_nmda) * V - g_gaba_a (V-V_gaba_a)
i.e. the reversal potential for ampa and nmda is 0.

Two state kinetic scheme synapse described by rise time tau1,
and decay time constant tau2. The normalized peak conductance is 1.
Decay time, tau2, MUST be greater than rise time, tau1.

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 used to be evaluated in the
initial block such that an event of weight 1 generates a
peak conductance of 1, however now it is set so that a peak
conductance of tau2*exp(-1) is reached because that's what the
Traub alpha function (t-t_0)*exp(-(t-t_0)/tau) reaches..

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.


	POINT_PROCESS AMPA  : since only used for ampa, a preferable name to AlphaSynDiffEqT
	RANGE tau, e, i : tau1 removed from RANGE because under program cntrl
			: what was tau2 was renamed tau for easy remembering
			: during use of this synapse

	GLOBAL near_unity, gfac

:for network debugging 
:	USEION ampa1 WRITE iampa1 VALENCE 0
:	USEION ampa2 WRITE iampa2 VALENCE 0
:	RANGE srcgid, targid, comp, synid

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

	near_unity = 0.999 (1) : tau1 tenth of a percent smaller than tau2 by default
	tau = 10 (ms) <1e-9,1e9>
	e=0	(mV)
	gfac = 1

	v (mV)
	i (nA)
	g (uS)
	tau1 (ms)

:	iampa1 (nA)
:	iampa2 (nA)
:	srcgid
:	targid
:	comp
:	synid

	A (uS)
	B (uS)

	tau1 = near_unity * tau
	A = 0
	B = 0
	tp = (tau1*tau)/(tau - tau1) * log(tau/tau1)
	factor = -exp(-tp/tau1) + exp(-tp/tau)
	factor = 1/factor
:	The above factor gives a peak conductance of 1
:	The above code is kept in place for comparison
:	This is modified though to return a peak value of tau*exp(-1)
:	(see FORTRAN code: f_traub = (t-t_0)*exp(-(t-t_0)/tau))
	factor = factor * tau * exp(-1)*1(/ms)

	SOLVE state METHOD cnexp
	g = B - A
	g = gfac*g
	i = g*(v - e)
:	iampa1 = g
:	iampa2 = -g

	A' = -A/tau1
	B' = -B/tau

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