Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)

 Download zip file 
Help downloading and running models
Accession:156780
"... We simulated detailed conductance-based models of TTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. ... Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex. "
Reference:
1 . Hay E, Segev I (2015) Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cereb Cortex 25:3561-71 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Dendrite;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I A, slow;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Glutamate;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Laminar Connectivity; Orientation selectivity;
Implementer(s): Hay, Etay [etay.hay at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; Glutamate; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I A, slow; Gaba; Glutamate;
/
HaySegev2014
models
readme.txt
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
ProbAMPANMDA2.mod *
ProbUDFsyn2.mod *
SK_E2.mod *
SKv3_1.mod *
cell1.asc *
microcircuit.hoc
                            
: this model is built-in to neuron with suffix epsp

COMMENT
modified from syn2.mod
injected current with exponential rise and decay current defined by
         i = 0 for t < onset and
         i=amp*((1-exp(-(t-onset)/tau0))-(1-exp(-(t-onset)/tau1)))
          for t > onset

	compare to experimental current injection:
 	i = - amp*(1-exp(-t/t1))*(exp(-t/t2))

	-> tau1==t2   tau0 ^-1 = t1^-1 + t2^-1
ENDCOMMENT
					       
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	POINT_PROCESS epsp
	RANGE onset, tau0, tau1, imax, i, myv
	NONSPECIFIC_CURRENT i
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
}

PARAMETER {
	onset=0  (ms)
	tau0=0.2 (ms)
	tau1=3.0 (ms)
	imax=0 	 (nA)
	v	 (mV)
}

ASSIGNED { i (nA)  myv (mV)}

LOCAL   a[2]
LOCAL   tpeak
LOCAL   adjust
LOCAL   amp

BREAKPOINT {
	myv = v
        i = curr(t)
}

FUNCTION myexp(x) {
	if (x < -100) {
	myexp = 0
	}else{
	myexp = exp(x)
	}
}

FUNCTION curr(x) {				
	tpeak=tau0*tau1*log(tau0/tau1)/(tau0-tau1)
	adjust=1/((1-myexp(-tpeak/tau0))-(1-myexp(-tpeak/tau1)))
	amp=adjust*imax
	if (x < onset) {
		curr = 0
	}else{
		a[0]=1-myexp(-(x-onset)/tau0)
		a[1]=1-myexp(-(x-onset)/tau1)
		curr = -amp*(a[0]-a[1])
	}
}

Loading data, please wait...