Current Dipole in Laminar Neocortex (Lee et al. 2013)

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Accession:151685
Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009. https://bitbucket.org/jonescompneurolab/corticaldipole
Reference:
1 . Lee S, Jones SR (2013) Distinguishing mechanisms of gamma frequency oscillations in human current source signals using a computational model of a laminar neocortical network. Front Hum Neurosci 7:869 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s): I Na,t; I K; I M; I Calcium; I h; I T low threshold; I K,Ca;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON (web link to model); Python (web link to model); NEURON; Python;
Model Concept(s): Magnetoencephalography; Temporal Pattern Generation; Activity Patterns; Gamma oscillations; Oscillations; Current Dipole; Touch;
Implementer(s): Lee, Shane [shane_lee at brown.edu];
Search NeuronDB for information about:  GabaA; GabaB; AMPA; NMDA; I Na,t; I T low threshold; I K; I M; I h; I K,Ca; I Calcium;
""" L2_basket.py - class def for layer 2 basket cells
    Copyright (C) 2013 Shane Lee and Stephanie Jones

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""

from neuron import h as nrn
from cell import BasketSingle

# Layer 2 basket cell class
class L2Basket(BasketSingle):
    """ Units for e: mV
        Units for gbar: S/cm^2 unless otherwise noted
    """
    def __init__(self, pos):
        # BasketSingle.__init__(self, pos, L, diam, Ra, cm)
        # Note: Basket cell properties set in BasketSingle())
        BasketSingle.__init__(self, pos, 'L2Basket')
        self.celltype = 'L2_basket'

        self.__synapse_create()
        self.__biophysics()

    # insert IClamps in all situations
    def create_all_IClamp(self, p):
        # list of sections for this celltype
        sect_list_IClamp = [
            'soma',
        ]

        # some parameters
        t_delay = p['Itonic_t0_L2Basket']

        # T = -1 means use nrn.tstop
        if p['Itonic_T_L2Basket'] == -1:
            t_dur = nrn.tstop - t_delay

        else:
            t_dur = p['Itonic_T_L2Basket'] - t_delay

        # t_dur must be nonnegative, I imagine
        if t_dur < 0.:
            t_dur = 0.

        # properties of the IClamp
        props_IClamp = {
            'loc': 0.5,
            'delay': t_delay,
            'dur': t_dur,
            'amp': p['Itonic_A_L2Basket']
        }

        # iterate through list of sect_list_IClamp to create a persistent IClamp object
        # the insert_IClamp procedure is in Cell() and checks on names
        # so names must be actual section names, or else it will fail silently
        # self.list_IClamp as a variable is guaranteed in Cell()
        self.list_IClamp = [self.insert_IClamp(sect_name, props_IClamp) for sect_name in sect_list_IClamp]

    # par connect between all presynaptic cells
    def parconnect(self, gid, gid_dict, pos_dict, p):
        """ no connections from L5Pyr or L5Basket to L2Baskets
        """
        # FROM L2 pyramidals TO this cell
        for gid_src, pos in zip(gid_dict['L2_pyramidal'], pos_dict['L2_pyramidal']):
            nc_dict = {
                'pos_src': pos,
                'A_weight': p['gbar_L2Pyr_L2Basket'],
                'A_delay': 1.,
                'lamtha': 3.,
            }

            self.ncfrom_L2Pyr.append(self.parconnect_from_src(gid_src, nc_dict, self.soma_ampa))

        # FROM other L2Basket cells
        for gid_src, pos in zip(gid_dict['L2_basket'], pos_dict['L2_basket']):
            nc_dict = {
                'pos_src': pos,
                'A_weight': p['gbar_L2Basket_L2Basket'],
                'A_delay': 1.,
                'lamtha': 20.,
            }

            self.ncfrom_L2Basket.append(self.parconnect_from_src(gid_src, nc_dict, self.soma_gabaa))

    # this function might make more sense as a method of net?
    def parreceive(self, gid, gid_dict, pos_dict, p_ext):
        """ par: receive from external inputs
        """
        # for some gid relating to the input feed:
        for gid_src, p_src, pos in zip(gid_dict['extinput'], p_ext, pos_dict['extinput']):
            # check if AMPA params are defined in the p_src
            if 'L2Basket_ampa' in p_src.keys():
                # create an nc_dict
                nc_dict_ampa = {
                    'pos_src': pos,
                    'A_weight': p_src['L2Basket_ampa'][0],
                    'A_delay': p_src['L2Basket_ampa'][1],
                    'lamtha': p_src['lamtha']
                }

                # AMPA synapse
                self.ncfrom_extinput.append(self.parconnect_from_src(gid_src, nc_dict_ampa, self.soma_ampa))

            # Check if NMDA params are defined in p_src
            if 'L2Basket_nmda' in p_src.keys():
                nc_dict_nmda = {
                    'pos_src': pos,
                    'A_weight': p_src['L2Basket_nmda'][0],
                    'A_delay': p_src['L2Basket_nmda'][1],
                    'lamtha': p_src['lamtha']
                }

                # NMDA synapse
                self.ncfrom_extinput.append(self.parconnect_from_src(gid_src, nc_dict_nmda, self.soma_nmda))

    # one parreceive function to handle all types of external parreceives
    def parreceive_ext(self, type, gid, gid_dict, pos_dict, p_ext):
        """ types must be defined explicitly here
        """
        if type.startswith(('evprox', 'evdist')):
            if self.celltype in p_ext.keys():
                gid_ev = gid + gid_dict[type][0]

                nc_dict = {
                    'pos_src': pos_dict[type][gid],
                    'A_weight': p_ext[self.celltype][0],
                    'A_delay': p_ext[self.celltype][1],
                    'lamtha': p_ext['lamtha_space'],
                }

                # connections depend on location of input
                if p_ext['loc'] is 'proximal':
                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict, self.soma_ampa))

                elif p_ext['loc'] is 'distal':
                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict, self.soma_ampa))
                    self.ncfrom_ev.append(self.parconnect_from_src(gid_ev, nc_dict, self.soma_nmda))

        elif type == 'extgauss':
            # gid is this cell's gid
            # gid_dict is the whole dictionary, including the gids of the extgauss
            # pos_list is also the pos of the extgauss (net origin)
            # p_ext_gauss are the params (strength, etc.)
            # I recognize this is ugly (hack)
            if self.celltype in p_ext.keys():
                # since gid ids are unique, then these will all be shifted.
                # if order of extgauss random feeds ever matters (likely)
                # then will have to preserve order
                # of creation based on gid ids of the cells
                # this is a dumb place to put this information
                gid_extgauss = gid + gid_dict['extgauss'][0]

                # gid works here because there are as many pos items in pos_dict['extgauss'] as there are cells
                nc_dict = {
                    'pos_src': pos_dict['extgauss'][gid],
                    'A_weight': p_ext[self.celltype][0],
                    'A_delay': p_ext[self.celltype][1],
                    'lamtha': p_ext['lamtha'],
                }

                self.ncfrom_extgauss.append(self.parconnect_from_src(gid_extgauss, nc_dict, self.soma_ampa))

        elif type == 'extpois':
            if self.celltype in p_ext.keys():
                gid_extpois = gid + gid_dict['extpois'][0]

                nc_dict = {
                    'pos_src': pos_dict['extpois'][gid],
                    'A_weight': p_ext[self.celltype][0],
                    'A_delay': p_ext[self.celltype][1],
                    'lamtha': p_ext['lamtha_space'],
                }

                self.ncfrom_extpois.append(self.parconnect_from_src(gid_extpois, nc_dict, self.soma_ampa))

        else:
            print("Warning, type def not specified in L2Basket")

    # insert biophysics
    def __biophysics(self):
        self.soma.insert('hh')

    # creation of synapses
    def __synapse_create(self):
        # creates synapses onto this cell
        self.soma_ampa = self.syn_ampa_create(self.soma(0.5))
        self.soma_gabaa = self.syn_gabaa_create(self.soma(0.5))
        self.soma_nmda = self.syn_nmda_create(self.soma(0.5))

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