Cortical Layer 5b pyr. cell with [Na+]i mechanisms, from Hay et al 2011 (Zylbertal et al 2017)

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Accession:230326
" ... Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca(2+) spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. ..."
References:
1 . Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol 7:e1002107 [PubMed]
2 . Zylbertal A, Yarom Y, Wagner S (2017) The Slow Dynamics of Intracellular Sodium Concentration Increase the Time Window of Neuronal Integration: A Simulation Study Front. Comput. Neurosci. 11(85):1-16 [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 L5/6 pyramidal GLU cell;
Channel(s): Na/Ca exchanger; Na/K pump; I Sodium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Detailed Neuronal Models; Action Potentials; Reaction-diffusion; Synaptic Plasticity; Active Dendrites; Olfaction;
Implementer(s): Zylbertal, Asaph [asaph.zylbertal at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Sodium; Na/Ca exchanger; Na/K pump;
# -*- coding: utf-8 -*-
"""
(C) Asaph Zylbertal 01.07.17, HUJI, Jerusalem, Israel
Based on: Hay, E., Hill, S., Schürmann, F., Markram, H., and Segev, I. (2011). Models of Neocortical Layer 5b Pyramidal
Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties. PLoS Comput. Biol. 7, e1002107. doi:10.1371/journal.pcbi.1002107.

Run a modified pyramidal cell model with a train of synaptic inputs in the dendritic hot zone, concurent with evoked calcium spikes
(article Fig 5E)

****************

"""


import neuron
import pyramidal
import numpy as np
import pickle
import matplotlib.pyplot as plt


def get_site(dist):
    sl = neuron.h.L5PC.locateSites('apic', dist)
    maxdiam = 0
    for i in range(len(sl)):
        dd1 = sl[i][1]
        sec = int(sl[i][0])
        dd = neuron.h.L5PC.apic[sec](dd1).diam
        if dd > maxdiam:
            j = i
            maxdiam = dd

    sec = int(sl[j][0])
    seg = sl[j][1]
    return neuron.h.L5PC.apic[sec](seg)


##########
syn_tau_onset = 0.5
syn_tau_offset = 10.
syn_gmax = 0.005
site_hot = 700.
DNa_coeff_dend = 0.1
soma_stim_amp = 3.0
#########

f = open('params', 'r')
params = pickle.load(f)
f.close()

py = pyramidal.pyramidal(
    params,
    space_factor=3,
    rest_file='rest_state',
    DNa_coeff_dend=DNa_coeff_dend)
py.convert_mechs()
syn_type = neuron.h.naSyn
stim_seg = get_site(site_hot).sec(0.5)

syns = []
soma_stims = []

for i in range(20):
    syns.append(syn_type(stim_seg))
    syns[-1].onset = 2000. + i * 200.
    syns[-1].tau_onset = syn_tau_onset
    syns[-1].tau_offset = syn_tau_offset
    syns[-1].gmax = syn_gmax

    if i < 8:
        soma_stims.append(neuron.h.IClamp(neuron.h.L5PC.soma[0](0.5)))
        soma_stims[-1].dur = 35
        soma_stims[-1].delay = 2020. + i * 200.
        soma_stims[-1].amp = soma_stim_amp

syns.append(syn_type(stim_seg))
syns[-1].onset = 7000.
syns[-1].tau_onset = syn_tau_onset
syns[-1].tau_offset = syn_tau_offset
syns[-1].gmax = syn_gmax

syns.append(syn_type(stim_seg))
syns[-1].onset = 15000.
syns[-1].tau_onset = syn_tau_onset
syns[-1].tau_offset = syn_tau_offset
syns[-1].gmax = syn_gmax

res = {}
res['t'] = neuron.h.Vector()
res['t'].record(neuron.h._ref_t)

res['v_soma'] = neuron.h.Vector()
res['v_soma'].record(neuron.h.L5PC.soma[0](0.5)._ref_v)
res['cai_dend'] = neuron.h.Vector()
res['cai_dend'].record(stim_seg._ref_cai)
res['nai_dend'] = neuron.h.Vector()
res['nai_dend'].record(stim_seg._ref_nai)
res['v_dend'] = neuron.h.Vector()
res['v_dend'].record(stim_seg._ref_v)
res['sec_len'] = stim_seg.sec.L

py.run_model(30000.)

plt.figure()
plt.subplot(311)
plt.plot(res['t'], res['v_dend'], label='Dendrite')
plt.plot(res['t'], res['v_soma'], label='Soma')
plt.legend()
plt.ylabel('Vm (mV)')
plt.subplot(312)
plt.plot(res['t'], res['nai_dend'])
plt.ylabel('Dend [Na+]i (mM)')
plt.subplot(313)
plt.plot(res['t'], 10e3 * np.array(res['cai_dend']))
plt.ylabel('Dend [Ca2+]i (uM)')

plt.savefig('fig.png')
plt.show()

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