Library of biophysically detailed striatal projection neurons (Lindroos and Hellgren Kotaleski 2020)

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Accession:266775
Library of compartmentalized models used to investigate dendritic integration in striatal projection neurons under neuromodulation.
Reference:
1 . Lindroos R, Hellgren Kotaleski J (2020) Predicting complex spikes in striatal projection neurons of the direct pathway following neuromodulation by acetylcholine and dopamine. Eur J Neurosci [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Striatum; Hippocampus; Basal ganglia;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell; Striatal projection neuron;
Channel(s): I M; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Acetylcholine; Dopamine;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Neuromodulation; Synaptic Plasticity; Activity Patterns; Soma-dendrite cross-talk;
Implementer(s): Lindroos, Robert [robert.lindroos at ki.se]; Filipovic, Marko;
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell; I M; I Potassium; Acetylcholine; Dopamine;
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lib
mechanisms
single
bk.mod *
cadyn.mod *
cal12.mod
cal13.mod
caldyn.mod *
can.mod
car.mod
cav32.mod
cav33.mod
gaba.mod
glutamate.mod
Im.mod *
kaf.mod
kas.mod
kdr.mod *
kir.mod
naf.mod
sk.mod
vecevent.mod *
                            
TITLE Fast A-type potassium current (Kv4.2)

COMMENT
neuromodulation is added as in two ways: 
1. A shift parameter is added to the infinity curves that can be used to shift the voltage
    dependece of the channel. By deafult this does not also shift the time constant.
2. A function that scales the conductance of the channel (voltage independent).


functions:
    
    modulation = 1 + damod*(maxMod-1)*level

where:
    
    damod  [0]: is a switch for turning modulation on or off {1/0}
    maxMod [1]: is the maximum modulation for this specific channel (read from the param file)
                e.g. 10% increase would correspond to a factor of 1.1 (100% +10%) {0-inf}
    level  [0]: is an additional parameter for scaling modulation. 
                Can be used simulate non static modulation by gradually changing the value from 0 to 1 {0-1}

[] == default values
{} == ranges


    
ENDCOMMENT

NEURON {
    THREADSAFE
    SUFFIX kaf
    USEION k READ ek WRITE ik
    RANGE gbar, gk, ik
    RANGE damod, maxMod, level, max2, lev2, modShift
}

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

PARAMETER {
    gbar = 0.0 (S/cm2) 
    q = 2
    damod = 0
    maxMod = 1
    level = 0
    max2 = 1
    lev2 = 0
    modShift = 0
} 

ASSIGNED {
    v (mV)
    ek (mV)
    ik (mA/cm2)
    gk (S/cm2)
    minf
    mtau (ms)
    hinf
    htau (ms)
}

STATE { m h }

BREAKPOINT {
    SOLVE states METHOD cnexp
    gk = gbar*m*m*h *modulation()
    ik = gk*(v-ek)
}

DERIVATIVE states {
    rates()
    m' = (minf-m)/mtau*q
    h' = (hinf-h)/htau*q
}

INITIAL {
    rates()
    m = minf
    h = hinf
}

PROCEDURE rates() {
    LOCAL alpha, beta, sum
    UNITSOFF
    alpha = 1.5/(1+exp((v-4+modShift)/(-17)))
    beta = 0.6/(1+exp((v-10+modShift)/9))
    sum = alpha+beta
    minf = alpha/sum
    mtau = 1/sum
    : mtau = 1/( 1.5/(1+exp((v-4)/(-17))) + 0.6/(1+exp((v-10)/9)) ) : don't shift tau
    
    alpha = 0.105/(1+exp((v-(-121)+modShift)/22))
    beta = 0.065/(1+exp((v-(-55)+modShift)/(-11)))
    sum = alpha+beta
    hinf = alpha/sum
    htau = 1/sum
    : htau = 1/( 0.105/(1+exp((v-(-121))/22)) + 0.065/(1+exp((v-(-55))/(-11))) ) : don't shift tau
    UNITSON
}

FUNCTION modulation() {
    : returns modulation factor
    
    modulation = 1 + damod * ( (maxMod-1)*level + (max2-1)*lev2 ) 
    if (modulation < 0) {
        modulation = 0
    }  
}

COMMENT

Original data by Tkatch (2000), P4-6 rat, 22 C.

Genesis implementation by Kai Du <kai.du@ki.se>, MScell v9.5.

Revision by Robert Lindroos <robert.lindroos@ki.se>, q factor applied
to both m and h instead of h only.

NEURON implementation by Alexander Kozlov <akozlov@csc.kth.se>.

ENDCOMMENT