Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)

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Accession:231427
This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
Reference:
1 . Doron M, Chindemi G, Muller E, Markram H, Segev I (2017) Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Cell Rep 21:1550-1561 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 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 Calcium; I A, slow;
Gap Junctions:
Receptor(s): NMDA; GabaA; AMPA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models;
Implementer(s): Doron, Michael [michael.doron at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow; Gaba; Glutamate;
/
reproduction
readme.txt
ampa.mod
Ca_HVA.mod
Ca_LVAst.mod *
cad.mod *
cadiffus.mod
CaDynamics_E2.mod *
canmda.mod *
car.mod *
gabaa.mod *
gabab.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
nmda.mod *
ProbAMPA.mod
ProbAMPANMDA2_ratio.mod
ProbUDFsyn2_lark.mod
SK_E2.mod *
SKv3_1.mod *
SynExp5NMDA.mod *
cell1.asc *
cellmorphology.hoc *
create_data_for_figure_01.py
create_data_for_figure_02.py
create_data_for_figure_03.py *
create_data_for_figure_03_control.py
create_data_for_figure_03_Dt_10.py *
create_data_for_figure_03_Dt_40.py *
data_same_excitation.pickle
iniparameter.hoc
L5PCbiophys3.hoc
L5PCbiophys3_noActive.hoc
mosinit.hoc
plot_figure_01.py
plot_figure_02.py
plot_figure_03.py
plot_figure_04.py
plot_figure_05.py
plot_figure_06.py
spikes_num.pickle
spine.hoc
TTC.hoc
                            
TITLE NMDA receptor--one of the two input stimulation of our model

: This mechanism is taken from the Neuron data base "exp2syn.mod" 
: The original comment are below between "COMMENT" and "ENDCOMMENT".
: 
: Our modifications:
: 
: 1.We added a single receptor conductance factor: "g_max=0.000045 (uS)".
:   An event of weight 1 generates a peak conductance of 1*g_max.
:   The weight is equal to the number of ampa receptors open at peak conductance
:
: 2.The NMDA receptors are simulated using a slow rise time constant 
:   and a double-expontial decay time constant

: The kinetic rate constants and channel conductance are taken from Franks KM, Bartol TM and Sejnowski TJ 
: A Monte Carlo model reveals independent signaling at central glutamatergic synapses 
: J Biophys (2002) 83(5):2333-48
: and Spruston N, Jonas P and Sakmann B
: Dendritic glutamate receptor channels in rat hippocampal CA3 and CA1 neurons
: J Physiol (1995) 482(2): 325-352
: correctd for physiological tempterature with Q10 from Hestrin S, Sah P and Nicoll RA  
: Mechanisms generating the time course of dual component excitatory synaptic currents 
: recorded in hippocampal slices
: Neuron (1990) 5: 247-253
:
: Written by Lei Tian on 04/12/06 



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 nmda
	RANGE tau1, tau2, tau3, e, i, g_max, g, A, B, C	,k
	NONSPECIFIC_CURRENT i
	GLOBAL total,i2,g2 
	EXTERNAL Area_canmda
}

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

PARAMETER {
	tau1 = 3.18     (ms) <1e-9,1e9>     :rise time constant
	tau2 = 57.14      (ms) <1e-9,1e9>	:decay time constant
	tau3 = 2000     (ms) <1e-9,1e9>	    :decay time constant
	
	g_max= 0.000045 (uS)			: single channel conductance
	e    = 0 (mV)
	mg   = 1 (mM)

	Area	(cm2)
	k = 1e-06 (mA/nA)
}

ASSIGNED {
	v (mV)
	i (nA)
	factor
	total (uS)
	g (uS)
	
	g2 (uS)	: plot 'g' and 'i' in "nmda.mod".
	i2 (mA/cm2)	: global variables read in "canmda.mod" as 'inmda' and 'gnmda' to give us
}

STATE {
	A (uS)
	B (uS)
	C (uS)
}

INITIAL {
	LOCAL t_peak
	total = 0
	if (tau1/tau2 > .9999) {
		tau1 = .9999*tau2
	}
	A = 0
	B = 0
	C = 0
	
	factor=0.8279	:from matlab to make the peak of the conductance curve shape to be 1*weight (then multiply with g_max)
	factor = 1/factor
	
	Area = Area_canmda 
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	
	g = g_max*(B*0.8+C*0.2-A)
	i = g*(v - e)*1/(1+(exp(0.08(/mV) * -v)*(mg / 0.69)))	
	
	g2=g			:global variable can be read in 'canmda.mod' 
	i2=i*k/Area		:to get a current in 'mA/cm2' and send it to 'canmda.mod'
}

DERIVATIVE state {
	A' = -A/tau1
	B' = -B/tau2
	C' = -C/tau3
}

NET_RECEIVE(weight (uS)) {
	state_discontinuity(A, weight*factor)
	state_discontinuity(B, weight*factor)
	state_discontinuity(C, weight*factor)
	total = total+weight
	
}


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