A model of optimal learning with redundant synaptic connections (Hiratani & Fukai 2018)

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Accession:225075
This is a detailed neuron model of non-parametric near-optimal latent model acquisition using multisynaptic connections between pre- and postsynaptic neurons.
Reference:
1 . Hiratani N, Fukai T (2018) Redundancy in synaptic connections enables neurons to learn optimally. Proc Natl Acad Sci U S A 115:E6871-E6879 [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 V1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity;
Implementer(s): Hiratani,Naoki [N.Hiratani at gmail.com];
Search NeuronDB for information about:  Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell;
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HirataniFukai2018
data
README.html
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caL3d.mod *
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exp2synNMDA.mod
h.mod *
HH2.mod *
kca.mod *
kir.mod *
km.mod *
kv.mod *
na.mod *
L23.hoc
libcell.py
md_readout.py
neuron_simulation.py
screenshot.png
                            
TITLE I-h channel from Magee 1998 for distal dendrites

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

}

PARAMETER {
    v (mV)
    ehd  (mV)        
    celsius (degC)
    ghdbar=.0001 (mho/cm2)
    vhalfl=-81   (mV)
    kl=-8
    vhalft=-75   (mV)
    a0t=0.011      (/ms)
    zetat=2.2    (1)
    gmt=.4   (1)
    q10=4.5
    qtl=1
}


NEURON {
    SUFFIX hd
    NONSPECIFIC_CURRENT i
        RANGE ghdbar, vhalfl
        GLOBAL linf,taul
}

STATE {
        l
}

ASSIGNED {
    i (mA/cm2)
        linf      
        taul
        ghd
}

INITIAL {
    rate(v)
    l=linf
}


BREAKPOINT {
    SOLVE states METHOD cnexp
    ghd = ghdbar*l
    i = ghd*(v-ehd)

}


FUNCTION alpt(v(mV)) {
  alpt = exp(0.0378*zetat*(v-vhalft)) 
}

FUNCTION bett(v(mV)) {
  bett = exp(0.0378*zetat*gmt*(v-vhalft)) 
}

DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rate(v)
        l' =  (linf - l)/taul
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-33)/10)
        a = alpt(v)
        linf = 1/(1 + exp(-(v-vhalfl)/kl))
:       linf = 1/(1+ alpl(v))
        taul = bett(v)/(qtl*qt*a0t*(1+a))
}






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