""" 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))