Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)

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Accession:141273
"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..."
Reference:
1 . Vierling-Claassen D, Cardin JA, Moore CI, Jones SR (2010) Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Front Hum Neurosci 4:198 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal intratelencephalic L2-6 cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,t; I T low threshold; I K; I M; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Detailed Neuronal Models; Brain Rhythms; Evoked LFP;
Implementer(s): Vierling-Claassen, Dorea ;
Search NeuronDB for information about:  Neocortex V1 pyramidal intratelencephalic L2-6 cell; I Na,t; I T low threshold; I K; I M; I h; I K,Ca; I Calcium;
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Vierling-ClaassenEtAl2010
batchsims
confiles
README
ar.mod
ca.mod *
cad.mod *
cat.mod
fdsexp2syn.mod *
Gfluct.mod *
gnetstim.mod
kca.mod *
km.mod *
kv.mod *
na_2.mod
light_batch_FSdrive.hoc
light_genconn_DB.m
lightgamma_drive_DB.hoc
lightgamma_init_DB.hoc
lightgamma_LFP_DB.hoc
lightgamma_localconn_DB.hoc
lightgamma_network_DB.hoc
lightgamma_noise_DB.hoc
lightgamma_wiring_DB.hoc
lightgamma_wiring_proc_DB.hoc
                            
COMMENT
Implementation of the model of short-term facilitation and depression described in
  Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., and Nelson, S.B.
  A quantitative description of short-term plasticity at excitatory synapses 
  in layer 2/3 of rat primary visual cortex
  Journal of Neuroscience 17:7926-7940, 1997
This is a modification of Exp2Syn that can receive multiple streams of 
synaptic input via NetCon objects.  Each stream keeps track of its own 
weight and activation history.

The printf() statements are for testing purposes only.


The synaptic mechanism itself uses a two state kinetic scheme 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 FDSExp2Syn
	RANGE tau1, tau2, e, i
	NONSPECIFIC_CURRENT i

	RANGE g
	GLOBAL total
        RANGE f, tau_F, d1, tau_D1, d2, tau_D2
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
}

PARAMETER {
	tau1 = 0.1 (ms) < 1e-9, 1e9 >
	tau2 = 10 (ms) < 1e-9, 1e9 >
	e = 0	(mV)
        : these values are from Fig.3 in Varela et al. 1997
	: the (1) is needed for the range limits to be effective
        f = 0.917 (1) < 0, 1e9 >    : facilitation
        tau_F = 94 (ms) < 1e-9, 1e9 >
        d1 = 0.416 (1) < 0, 1 >     : fast depression
        tau_D1 = 380 (ms) < 1e-9, 1e9 >
        d2 = 0.975 (1) < 0, 1 >     : slow depression
        tau_D2 = 9200 (ms) < 1e-9, 1e9 >
}

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

STATE {
	A (umho)
	B (umho)
}

INITIAL {
	LOCAL tp
	total = 0
	if (tau1/tau2 > 0.9999) {
		tau1 = 0.9999*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 (umho), F, D1, D2, tsyn (ms)) {
INITIAL {
: these are in NET_RECEIVE to be per-stream
        F = 1
        D1 = 1
        D2 = 1
        tsyn = t
: this header will appear once per stream
: printf("t\t t-tsyn\t F\t D1\t D2\t amp\t newF\t newD1\t newD2\n")
}

        F = 1 + (F-1)*exp(-(t - tsyn)/tau_F)
        D1 = 1 - (1-D1)*exp(-(t - tsyn)/tau_D1)
        D2 = 1 - (1-D2)*exp(-(t - tsyn)/tau_D2)
: printf("%g\t%g\t%g\t%g\t%g\t%g", t, t-tsyn, F, D1, D2, weight*F*D1*D2)
        tsyn = t

	state_discontinuity(A, A + weight*factor*F*D1*D2)
	state_discontinuity(B, B + weight*factor*F*D1*D2)
	total = total+weight*F*D1*D2

        F = F + f
        D1 = D1 * d1
        D2 = D2 * d2
: printf("\t%g\t%g\t%g\n", F, D1, D2)
}

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