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 316:344-366 [PubMed]
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 V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV 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 [samn at neurosim.downstate.edu]; McDougal, Robert [robert.mcdougal at yale.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV 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
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# $Id: pyinit.py,v 1.2 2012/02/15 16:22:52 samn Exp $ 

from neuron import h
import os
import sys
import datetime
import shutil
import pickle
from math import sqrt, pi
import numpy
import types

h("objref p")
h("p = new PythonObject()")

try:
    import pylab
    from pylab import plot, arange, figure
    my_pylab_loaded = True
except ImportError:
    print "Pylab not imported"
    my_pylab_loaded = False

def htype (obj): st=obj.hname(); sv=st.split('['); return sv[0]
def secname (obj): obj.push(); print h.secname() ; h.pop_section()
def psection (obj): obj.push(); print h.psection() ; h.pop_section()

allsecs=None #global list containing all NEURON sections, initialized via mkallsecs

# still need to generate a full allsecs
def mkallsecs ():
  """ mkallsecs - make the global allsecs variable, containing
      all the NEURON sections.
  """
  global allsecs
  allsecs=h.SectionList() # no .clear() command
  roots=h.SectionList()
  roots.allroots()
  for s in roots:
    s.push()
    allsecs.wholetree()
  return allsecs

#forall syntax - c gets executed, allsecs has Sections
def forall (c):
    """ NEURON forall syntax - iterates through all the sections available
        note that there's a dummy loop variable called s used in this function,
        so any command that needs to access a section should be via s.
        example: forall('print s.name()') , will print all the section names.
        Also note that this function uses a global list, 'allsecs', which may
        need to get re-initialized when new sections are created, via the mkallsecs
        function above.
    """
    global allsecs
    if (type(allsecs)==types.NoneType): mkallsecs()
    for s in allsecs: exec(c)

#forsec syntax - executes command for each section who's name
# contains secname as a substring
def forsec (secref="soma",command=""): 
    """ NEURON forsec syntax - iterates over all sections which have a substring
        in their names matching secref argument. command is executed if match found.
        this function also utilizes the allsecs global variable.
    """
    global allsecs
    if (type(allsecs)==types.NoneType): mkallsecs()
    if (type(secref)==types.StringTypes[0]):
        for s in allsecs:
            if s.name().count(secref) > 0:
                exec(command)
    else:
        for s in secref: exec(command)

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