Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)

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Accession:118631
Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008.
References:
1 . Koendgen H, Geisler C, Wang XJ, Fusi S, Luescher HR, Giugliano M (2004) The dynamical response of single cells to noisy time-varying currents Soc Neurosci Abstr :640
2 . Köndgen H, Geisler C, Fusi S, Wang XJ, Lüscher HR, Giugliano M (2008) The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex 18:2086-97 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Parameter Fitting; Methods; Rate-coding model neurons;
Implementer(s): Giugliano, Michele [mgiugliano at gmail.com]; Delattre, Vincent;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K;
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KoendgenEtAl2008
mechanisms
Isinunoisy.mod
Isinunoisy2.mod
wb.mod
                            
TITLE Wang Buzsaki Model
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:
:

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
    SUFFIX wb
    USEION na READ ena WRITE ina
    USEION k READ ek WRITE ik
    RANGE gnabar, gkbar, vth
    RANGE m_inf, h_inf, n_inf
    RANGE tau_h, tau_n
    RANGE h_exp, n_exp
}


UNITS {
    (mA) = (milliamp)
    (mV) = (millivolt)
}

PARAMETER {
    gnabar  = .07  (mho/cm2)
    gkbar   = .09  (mho/cm2)

    ena = 55    (mV)
    ek  = -90   (mV)
    celsius = 36    (degC)
    dt              (ms)
    v               (mV)
    
    vth = 0
}

STATE {
    h n
}

ASSIGNED {
    ina (mA/cm2)
    ik  (mA/cm2)
    il  (mA/cm2)
    m_inf
    h_inf
    n_inf
    tau_h
    tau_n
    h_exp
    n_exp
    tadj
}


BREAKPOINT {
    SOLVE states
    ina = gnabar * m_inf*m_inf*m_inf*h * (v - ena)
    ik  = gkbar * n*n*n*n * (v - ek)
}


:DERIVATIVE states {   : exact Hodgkin-Huxley equations
:   evaluate_fct(v)
:   m' = (m_inf - m) / tau_m
:   h' = (h_inf - h) / tau_h
:   n' = (n_inf - n) / tau_n
:}

PROCEDURE states() {    : exact when v held constant
    evaluate_fct(v)
    h = h + h_exp * (h_inf - h)
    n = n + n_exp * (n_inf - n)
    VERBATIM
    return 0;
    ENDVERBATIM
}

UNITSOFF
INITIAL {
    h = 0
    n = 0
:
:  Q10 was assumed to be 3 for both currents
:
: original measurements at roomtemperature?
:    tadj = 3.0 ^ ((celsius-36)/ 10 )
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b
    tadj = 1.
    
    if (v == -35) { a = 1 
    }else{          a = -0.1 * (v+35.) / ( exp(-(v+35)/10.) - 1) }
    b = 4. * exp(-(v+60.)/18.)    
    m_inf = a / (a + b)

    a = 0.007 * exp(-0.05*(v+58.))
    b = 1. / ( 1 + exp(-(v+28)/10.) )
    tau_h = (1. / (a + b)) / tadj
    h_inf = a / (a + b)

    if (v == -34) { a = 0.1 
    }else{          a = -0.01 * (v+34.) / ( exp(-(v+34)/10.) - 1) }
    b = 0.125 * exp(-0.0125*(v+44))
    tau_n = (1. / (a + b)) / tadj
    n_inf = a / (a + b)

    h_exp = 1 - exp(-dt/tau_h)
    n_exp = 1 - exp(-dt/tau_n)
}

UNITSON

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