Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON)

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Accession:226422
" ... In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. ..."
Reference:
1 . Kobayashi R, Tsubo Y, Shinomoto S (2009) Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front Comput Neurosci 3:9 [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): Multi-timescale adaptive threshold non-resetting leaky integrate and fire;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Parameter Fitting; Activity Patterns; Bursting;
Implementer(s): Appukuttan, Shailesh [shailesh.appukuttan at unic.cnrs-gif.fr; appukuttan.shailesh at gmail.com;];
NEURON {
    POINT_PROCESS MATmodel
    RANGE E_L, R, taum, tau1, tau2, omega, a_1, a_2, u1, u2, t_f, t_start, t_stop, I_amp, y, vth, I
}

UNITS {
    (mV) = (millivolt)
    (nA) = (nanoamp)
    (mO) = (megohm)
}

INITIAL {
    vm = E_L
    y = E_L
    vth = omega
    I = 0
    u1 = 0
    u2 = 0
    t_f = 0
}

PARAMETER {
    : Default values set for RS neurons
    E_L = -65  (mV)
    R = 50  (mO)
    taum = 5 (ms)
    tau1 = 10 (ms)
    tau2 = 200 (ms)

    omega = -45 (mV)
    a_1 = 30 (mV)
    a_2 = 2.0 (mV)

    t_f = 0 (ms)
    t_start = 100.0 (ms)
    t_stop = 600.0 (ms)
    I_amp = 0.6 (nA)
}

ASSIGNED {
    y (mV)
    vth (mV)
    I (nA)
}

STATE {
    vm (mV)
    u1 (mV)
    u2 (mV)
}

BREAKPOINT {
    vth = omega + u1 + u2
    y = vm

    if ((vm >= vth) && (t-t_f > 2.0)) {
        u1 = u1 + a_1
        u2 = u2 + a_2
        t_f = t
        y = 0
    }

    if ((t >= t_start) && (t <=t_stop)) {
        I = I_amp
    } else {
        I = 0
    }

    SOLVE states METHOD derivimplicit
}

DERIVATIVE states {
    vm' = (R*I-(vm-E_L))/(taum)
    u1' = -u1/tau1
    u2' = -u2/tau2
}

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