Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)

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Accession:144376
The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).
Reference:
1 . Winograd M, Destexhe A, Sanchez-Vives MV (2008) Hyperpolarization-activated graded persistent activity in the prefrontal cortex. Proc Natl Acad Sci U S A 105:7298-303 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Channel/Receptor;
Brain Region(s)/Organism: Prefrontal cortex (PFC);
Cell Type(s):
Channel(s): I Na,t; I L high threshold; I K; I M; I h;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Ion Channel Kinetics;
Implementer(s): Skolnick, Yosef [yskolnick at gmail.com];
Search NeuronDB for information about:  I Na,t; I L high threshold; I K; I M; I h;
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Skolnik_python_WinogradEtAl2008
readme.txt
APCounter2.mod *
Cadynamics.mod *
HH2.mod *
ICaL.mod *
Ih.mod *
IKM.mod *
ipulse3.mod *
pasi.mod *
demo_HPGA_non_saturating.py
demo_HPGA_non_saturating_noIh.py
demo_HPGA_saturating.py
geoms.py
pyinit.py
simgui.hoc
simgui.py
winograd.py
                            
# imports

from neuron import *
from geoms import *
h.load_file("nrngui.hoc")
#h.load_file("simgui.hoc")
h.nrnmainmenu()		#	// create main menu
h.nrncontrolmenu()



cells = [] # list of cells
ncl = [] # list of NetCons used to record cell action potentials (spikes)
ltimevec = [] # list of Vectors to record cell spikes
lidvec = []

# make the cells
def makeCells():
    cellID = 0
    cell = WinoSat(0,0,0,cellID) #Winograd Cells
    cells.append(cell)
def makeWinoSat():
    cellID = 0
    cell = WinoSat(0,0,0,cellID)
    cells.append(cell)
def makeWinNon():
    cellID = 0
    cell = WinNonS(0,0,0,cellID)
    cells.append(cell)
def makeWinNonIh():
    cellID = 0
    cell = WinNoIh(0,0,0,cellID)
    cells.append(cell)
def pinit(tstop =66000):
    h.dt=0.1
    h.tstop = tstop
    h.runStopAt = tstop
    h.steps_per_ms = 5
    h.celsius = 36
    h.v_init = -70

def winograph():
   
    h.newPlotS()
    h.newPlotS()
    h.newPlotS()
    
    h.newPlotS()
    h.newPlotS()
    h.newPlotS()
    h.Graph[0].addexpr('p.soma.cai')
    h.Graph[0].size(0,66000,0,.0063)
    h.Graph[1].addexpr('p.cells[0].APC.rate')
    h.Graph[1].size(0,66000,0,60)
    h.Graph[2].addexpr('p.cells[0].curr2.i')
    h.Graph[2].size(0,66000,-.3,.3)
    h.Graph[3].addexpr('p.soma.p1_iar')
    
    h.Graph[3].size(0,66000,0,.1)
    h.Graph[4].addexpr('p.soma.m_iar')
    h.Graph[4].size(0,66000,0,2)
    h.Graph[5].addexpr('p.soma.v')
    h.Graph[5].size(0,66000,-80,20)
    
def winographNoIh():
    h.newPlotS()
    h.newPlotS()
    h.newPlotS()

    h.newPlotS()
    #h.newPlotS()
    #h.newPlotS()
    h.Graph[0].addexpr('p.soma.cai')
    h.Graph[0].size(0,66000,0,.0063)
    h.Graph[1].addexpr('p.cells[0].APC.rate')
    h.Graph[1].size(0,66000,0,60)
    h.Graph[2].addexpr('p.cells[0].curr2.i')
    h.Graph[2].size(0,66000,-.3,.3)
   # h.Graph[3].addexpr('p.soma.p1_iar')

   # h.Graph[3].size(0,66000,0,.1)
   # h.Graph[4].addexpr('p.soma.m_iar')
   # h.Graph[4].size(0,66000,0,2)
    h.Graph[3].addexpr('p.soma.v')
    h.Graph[3].size(0,66000,-80,20)


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