def Biophys1(cell, template_name, dynamic_params):
"""Loads a biophysical NEURON hoc object using Cell-Types database objects."""
morphology_file = cell.morphology_file
hobj = h.Biophys1(str(morphology_file))
#fix_axon(hobj)
#set_params_peri(hobj, dynamic_params)
return hobj
def Biophys1_dict(cell):
""" Set parameters for cells from the Allen Cell Types database Prior to setting parameters will replace the
axon with the stub
"""
morphology_file = cell['morphology']
hobj = h.Biophys1(str(morphology_file))
return hobj
def Biophys1_adjusted(cell_prop):
morphology_file_name = str(cell_prop['morphology_file'])
hobj = h.Biophys1(morphology_file_name)
fix_axon_allactive(hobj)
# set_params_peri(hobj, cell_prop.model_params)
set_params(hobj, cell_prop.model_params)
return hobj
def Biophys1(cell_prop):
morphology_file_name = str(cell_prop['morphology_file'])
hobj = h.Biophys1(morphology_file_name)
fix_axon(hobj)
set_params_peri(hobj, cell_prop.model_params)
return hobj
def Biophys1_dict(cell):
""" Set parameters for cells from the Allen Cell Types database Prior to setting parameters will replace the
axon with the stub
"""
morphology_file = cell['morphology_file']
hobj = h.Biophys1(str(morphology_file))
fix_axon(hobj)
set_params_peri(hobj, cell.model_params)
return hobj
def __init__(self, swc_file, random_seed=10):
nrn.load_neuron_modules(None, None)
self._hobj = h.Biophys1(swc_file)
self._morphology = Morphology(self._hobj)
self._morphology.set_seg_props()
self._morphology.calc_seg_coords()
self._prng = np.random.RandomState(random_seed)
self._secs = []
self._save_sections()
def Biophys1(cell_prop):
'''
Set parameters for cells from the Allen Cell Types database
Prior to setting parameters will replace the axon with the stub
'''
morphology_file_name = str(cell_prop['morphology_file'])
hobj = h.Biophys1(morphology_file_name)
fix_axon(hobj)
# set_params_peri(hobj, cell_prop.model_params)
set_params(hobj, cell_prop.model_params)
return hobj
def Biophys1(cell_prop):
'''
Set parameters for cells from the Allen Cell Types database
Prior to setting parameters will replace the axon with the stub
'''
morphology_file_name = str(cell_prop['morphology_file'])
hobj = h.Biophys1(morphology_file_name)
fix_axon_allactive(hobj)
#fix_axon(hobj)
# set_params_peri(hobj, cell_prop.model_params)
set_params(hobj, cell_prop.model_params)
def calc_density_exp(dist):
# FILL IN HERE
g_max = 2.84922026765e-07
density = g_max * (-0.8696 + 2.087 * np.exp((dist) * 0.0031))
#return dist
return density
targeted_sections = ['apic'] # only apply to these types of segements
h.distance(
sec=hobj.soma[0]
) # need this to set all distances relative to soma (not sure if from center?)
for sec in hobj.all:
sec_type = sec.name().split(".")[1][:4]
if sec_type in targeted_sections:
# sec.insert('Ih_mod') # insert channel mechanics
for seg in sec:
dist = h.distance(seg.x)
sec_density = calc_density_exp(
dist) # calculate the channel density
setattr(seg, 'gbar_Ih_mod', sec_density) # 0.0166428509042)
for sec in hobj.all:
sec_type = sec.name().split(".")[1][:4]
if sec_type == 'axon':
continue
# print the distance!
#if sec_type in targeted_sections:
print sec.name()
for seg in sec:
print h.distance(seg.x), seg.gbar_Ih_mod
return hobj
def __init__(self, swc_path, fix_axon=True):
self._swc_path = swc_path
self._swc_df = pd.read_csv(
swc_path,
sep=' ',
names=['id', 'type', 'x', 'y', 'z', 'r', 'pid'],
comment='#')
nrn.load_neuron_modules(None, None)
self._hobj = h.Biophys1(swc_path)
self._sections = []
self._swc_ids = []
section_list = list(self._hobj.all)
for ln in range(len(self._swc_df)):
sec_id = int(self._hobj.nl.point2sec[ln])
swc_id = int(self._hobj.nl.pt2id(ln))
sec = section_list[sec_id]
self._sections.append(sec.name())
self._swc_ids.append(swc_id)
self._swc_df['section_name'] = self._sections
# self._swc_table = pd.DataFrame({
# 'section_name': self._sections,
# 'swc_id': self._swc_ids
# }).
if fix_axon:
self._fix_axon()
axon_indices = [
i for i, sec_name in enumerate(self._sections)
if 'axon' in sec_name
]
self._sections = [
self._sections[i] for i in range(len(self._sections))
if i not in axon_indices
]
self._swc_ids = [
self._swc_ids[i] for i in range(len(self._swc_ids))
if i not in axon_indices
]
def NMLLoad(cell, template_name, dynamic_params):
"""Convert a NEUROML file to a NEURON hoc cell object.
Current limitations:
* Ignores nml morphology section. You must pass in a swc file
* Only for biophysically detailed cell biophysical components. All properties must be assigned to a segment group.
:param cell:
:param template_name:
:param dynamic_params:
:return:
"""
# Last I checked there is no built in way to load a NML file directly into NEURON through the API, instead we have
# to manually parse the nml file and build the NEUROM cell object section-by-section.
morphology_file = cell.morphology_file
hobj = h.Biophys1(str(morphology_file))
# Depending on if the axon is cut before or after setting cell channels and mechanism can create drastically
# different results. Currently NML files doesn't produce the same results if you use model_processing directives.
# TODO: Find a way to specify model_processing directive with NML file
fix_axon_peri(hobj)
# Load the hoc template containing a swc initialized NEURON cell
if template_name in nml_files:
nml_params = nml_files[template_name]
else:
# Parse the NML parameters file xml tree and cache.
biophys_dirs = cell.network.get_component(
'biophysical_neuron_models_dir')
nml_path = os.path.join(biophys_dirs, template_name)
nml_params = NMLTree(nml_path)
nml_files[template_name] = nml_params
# Iterate through the NML tree by section and use the properties to manually create cell mechanisms
section_lists = [(sec, sec.name().split(".")[1][:4]) for sec in hobj.all]
for sec, sec_name in section_lists:
for prop_name, prop_obj in nml_params[sec_name].items():
if prop_obj.element_tag() == 'resistivity':
sec.Ra = prop_obj.value
elif prop_obj.element_tag() == 'specificCapacitance':
sec.cm = prop_obj.value
elif prop_obj.element_tag(
) == 'channelDensity' and prop_obj.ion_channel == 'pas':
sec.insert('pas')
setattr(sec, 'g_pas', prop_obj.cond_density)
for seg in sec:
seg.pas.e = prop_obj.erev
elif prop_obj.element_tag(
) == 'channelDensity' or prop_obj.element_tag(
) == 'channelDensityNernst':
sec.insert(prop_obj.ion_channel)
setattr(sec, prop_obj.id, prop_obj.cond_density)
if prop_obj.ion == 'na' and prop_obj:
sec.ena = prop_obj.erev
elif prop_obj.ion == 'k':
sec.ek = prop_obj.erev
elif prop_obj.element_tag() == 'concentrationModel':
sec.insert(prop_obj.id)
setattr(sec, 'gamma_' + prop_obj.type, prop_obj.gamma)
setattr(sec, 'decay_' + prop_obj.type, prop_obj.decay)
return hobj
