Fast-spiking cortical interneuron (Golomb et al. 2007)

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Accession:97747
Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.
Reference:
1 . Golomb D, Donner K, Shacham L, Shlosberg D, Amitai Y, Hansel D (2007) Mechanisms of firing patterns in fast-spiking cortical interneurons. PLoS Comput Biol 3:e156 [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): Neocortex fast spiking (FS) interneuron;
Channel(s): I Na,t; I K; I A, slow; I_KD;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; XPP;
Model Concept(s): Bursting; Action Potentials; Stuttering; Delay;
Implementer(s):
Search NeuronDB for information about:  I Na,t; I K; I A, slow; I_KD;
Files displayed below are from the implementation
/
fs_internrn
readme.html
fig2c.jpg
gah_fig2c.ode
                            
# numbers:
number Cm=1.0

# parameters:
p Iapp=3.35
p gA=0.39
p theta_m=-24.0
p gNa=112.5,gK=225.0,gL=0.25
p sigma_m=11.5
p theta_h=-58.3  , sigma_h=-6.7
p theta_n=-12.4  , sigma_n=6.8
p theta_t_h=-60  , sigma_t_h=-12.0
p theta_tna=-14.6, sigma_tna=-8.6
p theta_tnb=1.3  , sigma_tnb=18.7
p theta_a=-50    , sigma_a=20
p theta_b=-70    , sigma_b=-6
p tau_b=150, tau_a=2
p power_n=2.0
p V_Na=50.0,V_K=-90.0,V_L=-70.0

# auxilary functions:
GAMMAF(VV,theta,sigma)=1.0/(1.0+exp(-(VV-theta)/sigma))

# innitial conditions
V(0)=-70.038
h(0)=0.8522
n(0)=0.000208
a(0)=0.2686
b(0)=0.5016
# functions:
m_inf=GAMMAF(V,theta_m,sigma_m)
h_inf=GAMMAF(V,theta_h,sigma_h)
n_inf=GAMMAF(V,theta_n,sigma_n)
a_inf=GAMMAF(V,theta_a,sigma_a)
b_inf=GAMMAF(V,theta_b,sigma_b)
tau_h(V)=0.5+14.0*GAMMAF(V,theta_t_h,sigma_t_h)
tau_n(V)=(0.087+11.4*GAMMAF(V,theta_tna,sigma_tna))*(0.087+11.4*GAMMAF(V,theta_tnb,sigma_tnb))

# ode's
V'=-gNa*m_inf^3*h*(V-V_Na)-gK*(n^power_n)*(V-V_K)-gL*(V-V_L)-gA*a^3*b*(V-V_k)+Iapp
h'=(h_inf-h)/tau_h (V)
n'=(n_inf-n)/tau_n(V)
a'=(a_inf-a)/tau_a
b'=(b_inf-b)/tau_b

# 
@ MAXSTOR=16000000
@ BACK=Black
@ XP=T
@ YP=Vs
@ AXES=2
@ TOTAL=1000.0
@ DT=0.01
@ NJMP=1
@ T0=0.0
@ TRANS=0.0
@ NMESH=40
@ METH=rungekutta
@ DTMIN=0.001
@ DTMAX=1.0
@ TOLER=0.00001
@ BOUND=10000.0
@ DELAY=0.0
@ XLO=0.0, XHI=1000.0, YLO=-90.0, YHI=50.0
@ NTST=50,NMAX=20000,NPR=50
@ DS=0.02,DSMIN=0.001,DSMAX=0.5
@ PARMIN=-10,PARMAX=200,NORMMIN=0.0,NORMMAX=10000.0
@ AUTOVAR=V,AUTOXMIN=-10.0,AUTOXMAX=100.0,AUTOYMIN=-90.0,AUTOYMAX=90.0

done

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