Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)

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Accession:185858
"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ..."
Reference:
1 . Neymotin SA, McDougal RA, Bulanova AS, Zeki M, Lakatos P, Terman D, Hines ML, Lytton WW (2016) Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience 316:344-66 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Synapse; Channel/Receptor; Molecular Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Neocortex layer 2-3 interneuron; Neocortex layer 5 interneuron; Neocortex layer 6a interneuron;
Channel(s): I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I CAN; I Calcium; I_AHP; I_KD; Ca pump;
Gap Junctions:
Receptor(s): mGluR1; GabaA; GabaB; AMPA; NMDA; mGluR; Glutamate; Gaba; IP3;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Ion Channel Kinetics; Oscillations; Spatio-temporal Activity Patterns; Signaling pathways; Working memory; Attractor Neural Network; Calcium dynamics; Laminar Connectivity; G-protein coupled; Rebound firing; Brain Rhythms; Dendritic Bistability; Reaction-diffusion; Beta oscillations; Persistent activity; Multiscale;
Implementer(s): Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; McDougal, Robert [robert.mcdougal at yale.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; mGluR1; GabaA; GabaB; AMPA; NMDA; mGluR; Glutamate; Gaba; IP3; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I CAN; I Calcium; I_AHP; I_KD; Ca pump; Gaba; Glutamate;
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CaHDemo
readme.html
cagk.mod
cal.mod *
calts.mod *
can.mod *
cat.mod *
gabab.mod *
IC.mod *
icalts.mod *
Ih.mod
ihlts.mod *
IKM.mod *
kap.mod
kcalts.mod *
kdmc.mod
kdr.mod
kdrbwb.mod
km.mod *
mglur.mod *
misc.mod
MyExp2SynBB.mod *
MyExp2SynNMDABB.mod
nafbwb.mod
nax.mod
vecst.mod *
aux_fun.inc *
conf.py
declist.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
geom.py
ghk.inc *
grvec.hoc
init.hoc
labels.hoc
labels.py *
local.hoc *
misc.h
mpisim.py
netcfg.cfg
nqs.hoc
nqs.py
nrnoc.hoc *
onepyr.cfg
onepyr.py
pyinit.py *
python.hoc *
pywrap.hoc *
screenshot.png
screenshot1.png
simctrl.hoc *
simdat.py
syncode.hoc *
xgetargs.hoc *
                            
: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ 
NEURON {
:  THREADSAFE
  POINT_PROCESS MyExp2SynBB
  RANGE tau1, tau2, e, i, g, Vwt, gmax
  NONSPECIFIC_CURRENT i
}

UNITS {
  (nA) = (nanoamp)
  (mV) = (millivolt)
  (uS) = (microsiemens)
}

PARAMETER {
  tau1=.1 (ms) <1e-9,1e9>
  tau2 = 10 (ms) <1e-9,1e9>
  e=0	(mV)
  gmax = 1e9 (uS)
  Vwt   = 0 : weight for inputs coming in from vector
}

ASSIGNED {
  v (mV)
  i (nA)
  g (uS)
  factor
  etime (ms)
}

STATE {
  A (uS)
  B (uS)
}

INITIAL {
  LOCAL tp

  Vwt = 0    : testing

  if (tau1/tau2 > .9999) {
    tau1 = .9999*tau2
  }
  A = 0
  B = 0
  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
  factor = -exp(-tp/tau1) + exp(-tp/tau2)
  factor = 1/factor
}

BREAKPOINT {
  SOLVE state METHOD cnexp
  g = B - A
  if (g>gmax) {g=gmax}: saturation
  i = g*(v - e)
}

DERIVATIVE state {
  A' = -A/tau1
  B' = -B/tau2
}

NET_RECEIVE(w (uS)) {LOCAL ww
  ww=w
  A = A + ww*factor
  B = B + ww*factor
}