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]
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 Delayed rectifying potassium current

NEURON {
    SUFFIX kdr
    USEION k READ ek WRITE ik
    RANGE gbar, gk, ik
}

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

PARAMETER {
    gbar = 0.0 (S/cm2) 
    q = 3
}

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

STATE { m }

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

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

INITIAL {
    rates()
    m = minf
}

PROCEDURE rates() {
    LOCAL alpha, beta, sum
    UNITSOFF
    alpha = 1.0*exp((v-(-13))/(-9.09))
    beta =  1.0*exp((v-(-13))/(-12.5))
    sum = alpha+1
    minf = 1/sum
    mtau = 50*beta/sum
    UNITSON
}

COMMENT

Original data by Migliore (1999), rat CA1, 22 C.

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

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

ENDCOMMENT

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