Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)

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Accession:150217
We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.
Reference:
1 . Zeldenrust F, Chameau PJ, Wadman WJ (2013) Reliability of spike and burst firing in thalamocortical relay cells. J Comput Neurosci 35:317-34 [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:
Cell Type(s): Abstract Morris-Lecar neuron;
Channel(s): I Na,t; I T low threshold; I K; I h; I_AHP;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: XPP; MATLAB;
Model Concept(s): Bursting; Reliability;
Implementer(s): Zeldenrust, Fleur [fleurzeldenrust at gmail.com];
Search NeuronDB for information about:  I Na,t; I T low threshold; I K; I h; I_AHP;
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xpp_code
prescott
prescott_noisestim.ode
runchange.m
                            
# Modified Morris-Lecar model from Prescott (2008, 2008) 
# modified from ml_salka.ode

#stim used in experiments, mean=0, std=0.1
table Iext stim.tab
#Iext(t)=0

nd=normal(0,0.3)
par dc_noise=2.2218
aux noise=dc_noise+nd

dV/dt = (i_dc(t)+amp*Iext(t)+dc_noise+nd-gna*minf(V)*(V-Vna)-gk*y*(V-VK)-gl*(V-Vl))/c
# dy/dt = phi_y*(yinf(V)-y)/tauy(V)
dy/dt = if(y<0)then(0.1)else(if(y>1)then(-0.1)else(phi_y*(yinf(V)-y)/tauy(V)))
par c=2


i_dc(t)=idc
# idc is -20.42 voor -80, -1.15 voor -70, 16.8 voor -60, 31.25 voor -50
par idc=32
init V=-50, y=0

par amp=150
aux stim=i_dc(t)+amp*Iext(t)



# FAST INWARD CURRENT (INa or activation variable)
# This is assumed to activate instantaneously with changes in voltage
# voltage-dependent activation curve is described by m
minf(V)=.5*(1+tanh((V-beta_m)/gamma_m))
# maximal conductance and reversal potential
par beta_m=-1.2,gamma_m=18
par gna=20,vna=50

# DELAYED RECTIFIER CURRENT (IKdr or recovery variable)
# this current activates more slowly than INa
# In this code, activation of IKdr is controlled by y
yinf(V)=.5*(1+tanh((V-beta_y)/gamma_y))
tauy(V)=1/cosh((V-beta_y)/(2*gamma_y))
# in the 2D model, varying beta_w shifts the w activation curve (w=y here) and can convert the neuron between class 1, 2, and 3 
par beta_y=0, gamma_y=10
# maximal conductance and reversal potential
par gk=20, vk=-100, phi_y=0.15

# LEAK CURRENT (Il)
# just a passive leak conductance
par gl=2, vl=-70

# following parameters control duration of simulation and axes of default plot
@ total=303000,xlo=0,xhi=6000,ylo=-100,yhi=50
@ meth=euler, dt=0.1, bounds=1000     
@ MAXSTOR=3030010

done

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