def __init__(self, name, compile_paths=model_path):
"""
Experimental cell of Hay 2011 rewrited to Python
:param name:
:param compile_paths:
"""
Cell.__init__(self, name=name, compile_paths=compile_paths)
def __init__(self, name=None, compile_paths=None):
"""
:param name:
Name of the cell
:param compile_paths:
paths to folders containing mods. Can be list or string separated by spaces.
"""
CoreCell.__init__(self, name=name, compile_paths=compile_paths)
Cell.__init__(self, name)
CoreHocCell.__init__(self, name)
def __init__(self, name=None, model_folder=f_path, spine_number=0,
spine_secs_names="apic",
spine_seed: int = None):
"""
:param name:
The name of the cell
:param model_folder:
The folder where the main folder of Combe et al. 2018 model is located
:param spine_number:
The number of spines added to the model with random_uniform distribution to the sections
specified by 'spine_sec' param.
:param spine_secs_names:
The section or sections where to put spines. It can be:
* a string - as a filter name, so you can set "apic" to add spies to all apical
dendrites
* a regex, which need to be prefixed with 'regex:' string before
eg. 'regex:(apic)|(basal)'
will return all sections wich have a name containing 'apic' or 'basal' string
* a list of existing sections in the cell
:param spine_seed:
Seed value for the random_uniform spike distribution. Default is None
meaning - there is no seed
"""
Cell.__init__(self, name, model_folder)
CoreHocCell.__init__(self, name)
main_file = os.path.join(model_folder, "load_cell.hoc")
self.load_hoc(main_file)
secs = self.filter_secs(name=spine_secs_names)
# Add spines with AMPA and NMDA synapses
self.combe_syns = []
if spine_number > 0:
self.add_randuniform_spines(secs=secs, spine_number=spine_number, head_nseg=10,
neck_nseg=10, seed=spine_seed)
# Copy mechanisms from parent sec of the neck and from the nec to the head
self.copy_mechanisms(secs_to=self.necks, sec_from='parent')
self.copy_mechanisms(secs_to=self.heads, sec_from='parent')
ampa_syns = []
nmda_syns = []
# Create AMPA synapses
ampa_weight = 1.2 * 0.00156
for h in self.heads:
syn = self.add_synapse(source=None, seg=h(1.0), mod_name="Exp2Syn",
netcon_weight=ampa_weight)
syn.point_process.hoc.e = 0
syn.point_process.hoc.tau1 = .5
syn.point_process.hoc.tau2 = 1.0
ampa_syns.append(syn)
# Create NMDA synapses
nmda_weight = 1.2 * 0.000882
for h in self.heads:
syn = self.add_synapse(source=None, seg=h(1.0), mod_name="nmdanet",
netcon_weight=nmda_weight)
syn.point_process.hoc.Alpha = 0.35
syn.point_process.hoc.Beta = 0.035
nmda_syns.append(syn)
for syns in zip(ampa_syns, nmda_syns):
comp_syn = self.group_synapses(synapses=syns, tag="combe_type")
self.combe_syns.append(comp_syn)
def __init__(self, name=None, model_folder=model_path):
Cell.__init__(self, name, model_folder)
CoreHocCell.__init__(self, name)
main_file = os.path.join(model_folder, "run_PC.hoc")
self.load_hoc(main_file, cell_template_name="PyramidalCell")
