Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)

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Accession:231427
This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
Reference:
1 . Doron M, Chindemi G, Muller E, Markram H, Segev I (2017) Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Cell Rep 21:1550-1561 [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: Neocortex;
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Receptor(s): NMDA; GabaA; AMPA;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models;
Implementer(s): Doron, Michael [michael.doron at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow; Gaba; Glutamate;
/
reproduction
readme.txt
ampa.mod
Ca_HVA.mod
Ca_LVAst.mod *
cad.mod *
cadiffus.mod
CaDynamics_E2.mod *
canmda.mod *
car.mod *
gabaa.mod *
gabab.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
nmda.mod *
ProbAMPA.mod
ProbAMPANMDA2_ratio.mod
ProbUDFsyn2_lark.mod
SK_E2.mod *
SKv3_1.mod *
SynExp5NMDA.mod *
cell1.asc *
cellmorphology.hoc *
create_data_for_figure_01.py
create_data_for_figure_02.py
create_data_for_figure_03.py *
create_data_for_figure_03_control.py
create_data_for_figure_03_Dt_10.py *
create_data_for_figure_03_Dt_40.py *
data_same_excitation.pickle
iniparameter.hoc
L5PCbiophys3.hoc
L5PCbiophys3_noActive.hoc
mosinit.hoc
plot_figure_01.py
plot_figure_02.py
plot_figure_03.py
plot_figure_04.py
plot_figure_05.py
plot_figure_06.py
spikes_num.pickle
spine.hoc
TTC.hoc
                            
#!/usr/bin/python

from neuron import h
from neuron import gui
import matplotlib as mpl
mpl.use("Agg")
import matplotlib.pyplot as plt
font = {'weight':'regular', 'size':8, 'family':'sans-serif', 'sans-serif':'Arial'}
mpl.rc('font', **font)
mpl.rcParams['text.usetex']=True
mpl.rcParams['text.latex.unicode']=True

import numpy as np
import sys
import pickle
import time
import itertools
import numpy as np

def indexes(y, thres=0.3, min_dist=1):
    if isinstance(y, np.ndarray) and np.issubdtype(y.dtype, np.unsignedinteger):
        raise ValueError("y must be signed")
    thres = thres * (np.max(y) - np.min(y)) + np.min(y)
    min_dist = int(min_dist)
    dy = np.diff(y)
    zeros,=np.where(dy == 0)
    
    while len(zeros):
        zerosr = np.hstack([dy[1:], 0.])
        zerosl = np.hstack([0., dy[:-1]])

        dy[zeros]=zerosr[zeros]
        zeros,=np.where(dy == 0)

        dy[zeros]=zerosl[zeros]
        zeros,=np.where(dy == 0)

    peaks = np.where((np.hstack([dy, 0.]) < 0.)
                     & (np.hstack([0., dy]) > 0.)
                     & (y > thres))[0]

    if peaks.size > 1 and min_dist > 1:
        highest = peaks[np.argsort(y[peaks])][::-1]
        rem = np.ones(y.size, dtype=bool)
        rem[peaks] = False

        for peak in highest:
            if not rem[peak]:
                sl = slice(max(0, peak - min_dist), peak + min_dist + 1)
                rem[sl] = True
                rem[peak] = False

        peaks = np.arange(y.size)[~rem]
    return peaks

eSynlist = []
ePreconlist = []
iPreconlist = []
eStimlist = []
iStimlist = []
iSynlista = []

def placeNMDA(location):
  eStimlist.append(h.NetStim())
  eStimlist[-1].interval = 1
  eStimlist[-1].number = 1
  eStimlist[-1].start = 100
  eStimlist[-1].noise = 0
  eSynlist.append(h.ProbAMPANMDA2_RATIO(location))
  eSynlist[-1].gmax = 0.0004
  eSynlist[-1].mgVoltageCoeff = 0.08
  ePreconlist.append(h.NetCon(eStimlist[-1], eSynlist[-1]))
  ePreconlist[-1].weight[0] = 1
  ePreconlist[-1].delay = 0


def placeGABA(location):
  iStimlist.append(h.NetStim())
  iStimlist[-1].interval = 1
  iStimlist[-1].number = 1
  iStimlist[-1].start = 100
  iStimlist[-1].noise = 0          
  
  iSynlista.append(h.ProbUDFsyn2_lark(location))
  iSynlista[-1].tau_r = 0.18
  iSynlista[-1].tau_d = 5
  iSynlista[-1].e = - 80
  iSynlista[-1].Dep = 0
  iSynlista[-1].Fac = 0
  iSynlista[-1].Use = 0.6
  iSynlista[-1].u0 = 0
  iSynlista[-1].gmax = 0.001
  
  iPreconlist.append(h.NetCon(iStimlist[-1], iSynlista[-1]))
  iPreconlist[-1].weight[0] = 1
  iPreconlist[-1].delay = 0

def plot_figure_06_b_c():
  startTime = time.time()
  h.tstop = 300
  h.v_init = -70
  h.dt = 0.025

  delays = [0, 90, 100, 110, 120, 130]

  ampaCond = 0.008
  gabaCond = 0.0015
  seg = 0.9

  secNames = []
  secRins = []
  for sec in h.L5PC.all:
    a = h.SectionRef(sec=sec)
    if (a.nchild() == 0) and ('dend' in sec.name()):
      im = h.Impedance()
      h("access " + sec.name())
      im.loc(seg)
      im.compute(0)
      Ri = im.input(seg) 
      if (Ri > 400):
        secNames.append(sec.name())
        secRins.append(Ri)

  secRins, excitable_secs = zip(*sorted(zip(secRins, secNames)))
  num_of_branches = 16

  sectionNum = "L5PC.soma[0]"

  no_zero = True

  excitable_secs = ['TTC[0].dend[82]', 'TTC[0].dend[69]', 'TTC[0].dend[80]',
         'TTC[0].dend[66]', 'TTC[0].dend[10]', 'TTC[0].dend[66]',
         'TTC[0].dend[82]', 'TTC[0].dend[66]', 'TTC[0].dend[37]',
         'TTC[0].dend[54]', 'TTC[0].dend[31]', 'TTC[0].dend[72]',
         'TTC[0].dend[54]', 'TTC[0].dend[49]', 'TTC[0].dend[76]',
         'TTC[0].dend[13]']

  for syn in eSynlist:
    syn.gmax = 0
    del syn
  for syn in iSynlista:
    syn.gmax = 0
    del syn
  for stim in iStimlist:
    del stim 
  for sec in h.L5PC.all:
    h("nseg = 1")
  for sec in excitable_secs:
    h("access " + str(sec))
    h("nseg = 10")
    placeNMDA(seg)
    placeGABA(seg)
    h.pop_section()
  voltageNpVec = {}
  soma_voltageNpVec = {}
  for delay in delays:
    inhibitionTiming = delay
    voltageVec = h.Vector()
    soma_voltageVec = h.Vector()
    timeVec = h.Vector()
    for syn in eSynlist:
      syn.gmax = ampaCond
    for syn in iSynlista:
      syn.gmax = gabaCond
    for stim in iStimlist:
      stim.start = inhibitionTiming
    voltageVec.record(eval("h." + excitable_secs[-14] + "(" + str(seg) + ")._ref_v"))
    soma_voltageVec.record(eval("h." + sectionNum + "(" + str(seg) + ")._ref_v"))
    timeVec.record(h._ref_t)
    h.init()
    h.run()
    soma_voltageNpVec[delay] = np.array(soma_voltageVec)
    voltageNpVec[delay] = np.array(voltageVec)
    print len(indexes(soma_voltageNpVec[delay], thres=np.abs(10 - np.min(soma_voltageNpVec[delay])) / (np.abs(np.max(soma_voltageNpVec[delay]) - np.min(soma_voltageNpVec[delay]))), min_dist=10))
  len_orinial_spike_indices = len(indexes(soma_voltageNpVec[0], thres=np.abs(10 - np.min(soma_voltageNpVec[0])) / (np.abs(np.max(soma_voltageNpVec[0]) - np.min(soma_voltageNpVec[0]))), min_dist=10))
  len_spike_indices = len(indexes(soma_voltageNpVec[120], thres=np.abs(10 - np.min(soma_voltageNpVec[120])) / (np.abs(np.max(soma_voltageNpVec[120]) - np.min(soma_voltageNpVec[120]))), min_dist=10))
  if (len_spike_indices == 0 and len_orinial_spike_indices == 2): 
    no_zero = False

  colors = {90: 'black', 100: 'green', 110 : 'brown', 120 : 'blue', 130 : 'red'}

  for delay in delays[1:]:
    fig = plt.figure(figsize=(4, 6), frameon = False)
    ax = plt.Axes(fig, [0., 0., 1., 1.], )
    fig.add_axes(ax)

    ax.plot(timeVec, soma_voltageNpVec[delay], linewidth = 0.75, color='black');
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_visible(False)  
    ax.yaxis.set_ticks_position('left')
    ax.xaxis.set_ticks_position('bottom')       
    ax.axis([80, 200, -90, 60])
    ax.set_xticks([])
    ax.set_yticks([])
    if (delay == 130):
      ax.plot([150, 170],[-18, -18],linewidth=0.75,color='black')
      ax.plot([170, 170],[-18, 2],linewidth=0.75,color='black')
      ax.text(148, -44, "20 msec", fontsize = 8)
      ax.text(173, -16, "20 mV", fontsize = 8)
    fig.set_size_inches(0.6, 0.6)
    fig.savefig('figure_06_b_%d.pdf' % (delay), transparent=True, bbox_inches='tight', format='pdf', dpi=3000, pad_inches=0)
    plt.show(block = 0)

  for delay in delays[1:]:
    fig = plt.figure(figsize=(4, 6), frameon = False)
    ax = plt.Axes(fig, [0., 0., 1., 1.], )
    fig.add_axes(ax)
    ax.plot([delay, delay],[voltageNpVec[0][delay / 0.025], 0],linewidth=0.5,color='black',linestyle=':')
    ax.plot(timeVec, voltageNpVec[delay], linewidth = 0.75, color='green');
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_visible(False)  
    ax.yaxis.set_ticks_position('left')
    ax.xaxis.set_ticks_position('bottom')       
    ax.axis([80, 200, -90, 30])
    ax.set_xticks([])
    ax.set_yticks([])
    if (delay == 130):
      ax.plot([150, 170],[-18, -18],linewidth=0.75,color='black')
      ax.plot([170, 170],[-18, 2],linewidth=0.75,color='black')
      ax.text(148, -44, "20 msec", fontsize = 8)
      ax.text(173, -16, "20 mV", fontsize = 8)
    plt.text(delay - 14, 20, '%d msec' % (delay - 100), color='green', fontsize = 8)
    ax.arrow(delay, 10, 0, -10, head_width=1.25, width=0.75, head_length=0.75, fc='green', ec='green')
    fig.set_size_inches(0.6, 0.6)
    fig.savefig('figure_06_c_%d.pdf' % (delay), transparent=True, bbox_inches='tight', format='pdf', dpi=3000, pad_inches=0)
  # plt.close('all')
    


def plot_figure_06_d():
  eps = np.finfo(float).eps
  (avg, std) = pickle.load(open('spikes_num.pickle', 'rb'))
  delays = [0] + range(90, 161, 1)

  fig = plt.figure(figsize=(1.7,1.7))
  ax = plt.Axes(fig, [0., 0., 1., 1.], )
  fig.add_axes(ax)

  ax.plot([delays[1], delays[-1]], [avg[0],avg[0]], linewidth = 1, color="black", linestyle='--', dashes=(3,3)); 
  ax.plot(delays[1:], avg[1:], linewidth = 1, color="black"); 
  ax.plot(delays[1:], avg[1:] + std[1:], linewidth = 1, color="green"); 
  ax.plot(delays[1:], avg[1:] - std[1:], linewidth = 1, color="green"); 
  ax.fill_between(delays[1:], avg[1:] + std[1:], avg[1:] - std[1:],facecolor='green', alpha=0.2)

  ax.spines['top'].set_visible(False)
  ax.spines['right'].set_visible(False)
  ax.spines['left'].set_linewidth(1)
  ax.spines['bottom'].set_linewidth(1)
  ax.tick_params(direction='out', width=1, size=5)
  ax.yaxis.set_ticks_position('left')
  ax.xaxis.set_ticks_position('bottom')   
  ax.set_xlabel(r'${\Delta t}$ Inhibition vs excitation (msec)', fontsize = 10, fontweight = 'regular')
  ax.set_ylabel('Number of spikes', fontsize = 10, fontweight = 'regular')
  ax.axis([90, 160, -0.1, 3.25], fontsize = 24)
  kwargs = dict(linewidth = 1, transform=ax.transAxes, color='k', clip_on=False)
  ax.xaxis.set_ticks([100, 120, 140, 160])
  ax.xaxis.set_ticklabels([0, 20, 40, 60], fontsize=8)
  ax.yaxis.set_ticks([0,1,2,3])
  ax.yaxis.set_ticklabels([0,1,2,3], fontsize=8)
  plt.subplots_adjust(hspace = 0.1) 
  fig.set_size_inches(1.5, 1.5)
  fig.savefig('figure_06_d.pdf', transparent=True, bbox_inches='tight', format='pdf', dpi=300, pad_inches=0)


h.load_file("nrngui.hoc")
h.load_file("import3d.hoc")
h.load_file("L5PCbiophys3.hoc")
h.load_file("TTC.hoc")

h("objref L5PC")
h("celsius = 34.5")
h.L5PC = h.TTC("cell1.asc")
h("forall nseg = 1")
h("forall vo = 1")
h("objref eSynlist, ePreconlist, iPreconlist, eStimlist, iStimlist, iSynlista, voltageClamp, resistanceVector")

plot_figure_06_b_c()
plot_figure_06_d()

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