def synapse_test(template_class, gid, tree, synapses_dict, connections, v_init, env, unique=True):
postsyn_name = 'HCC'
presyn_names = ['MPP', 'LPP', 'GC', 'MC', 'HCC', 'IS', 'NGFC', 'MOPP']
cell = network_clamp.load_cell(env, postsyn_name, gid, \
tree_dict=tree, synapses_dict=synapses_dict, connections=connections, \
correct_for_spines=True, load_connections=False)
cells.register_cell(env, postsyn_name, gid, cell)
cells.report_topology(cell, env)
syn_attrs = env.synapse_attributes
syn_count, nc_count = synapses.config_biophys_cell_syns(env, gid, postsyn_name, unique=unique, \
insert=True, insert_netcons=False)
for presyn_name in presyn_names:
syn_params_dict = env.connection_config[postsyn_name][presyn_name].mechanisms
syn_filters = synapses.get_syn_filter_dict(env, {'sources': [presyn_name]}, convert=True)
syn_obj_dict = syn_attrs.filter_synapses(gid, **syn_filters)
v_holding = -60
synapse_group_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 1, v_holding, v_init)
synapse_group_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 10, v_holding, v_init)
synapse_group_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 100, v_holding, v_init)
rate = 20
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 1, rate)
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 10, rate)
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 20, rate)
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 50, rate)
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 100, rate)
synapse_group_rate_test(env, presyn_name, gid, cell, syn_obj_dict, syn_params_dict, 250, rate)
def init_biophys_cell(env, pop_name, gid, load_connections=True, register_cell=True, write_cell=False,
cell_dict={}):
"""
Instantiates a BiophysCell instance and all its synapses.
:param env: an instance of env.Env
:param pop_name: population name
:param gid: gid
"""
rank = int(env.pc.id())
## Determine if a mechanism configuration file exists for this cell type
if 'mech_file_path' in env.celltypes[pop_name]:
mech_dict = env.celltypes[pop_name]['mech_dict']
else:
mech_dict = None
## Determine if correct_for_spines flag has been specified for this cell type
synapse_config = env.celltypes[pop_name]['synapses']
if 'correct_for_spines' in synapse_config:
correct_for_spines_flag = synapse_config['correct_for_spines']
else:
correct_for_spines_flag = False
## Determine presynaptic populations that connect to this cell type
presyn_names = env.projection_dict[pop_name]
## Load cell gid and its synaptic attributes and connection data
cell = load_biophys_cell(env, pop_name, gid, mech_dict=mech_dict, \
correct_for_spines=correct_for_spines_flag, \
load_connections=load_connections,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dicts=cell_dict.get('weight', None))
if register_cell:
cells.register_cell(env, pop_name, gid, cell)
cells.report_topology(cell, env)
env.cell_selection[pop_name] = [gid]
if is_interactive:
context.update(locals())
if write_cell:
write_selection_file_path = "%s/%s_%d.h5" % (env.results_path, env.modelName, gid)
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
io_utils.write_cell_selection(env, write_selection_file_path)
if load_connections:
io_utils.write_connection_selection(env, write_selection_file_path)
return cell
def main(gid, pop_name, config_file, template_paths, hoc_lib_path, dataset_prefix, config_prefix, mech_file,
load_edges, load_weights, correct_for_spines, verbose):
"""
:param gid: int
:param pop_name: str
:param config_file: str; model configuration file name
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param hoc_lib_path: str; path to directory containing required hoc libraries
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
:param mech_file: str; cell mechanism config file name
:param load_edges: bool; whether to attempt to load connections from a neuroh5 file
:param load_weights: bool; whether to attempt to load connections from a neuroh5 file
:param correct_for_spines: bool
:param verbose: bool
"""
utils.config_logging(verbose)
logger = utils.get_script_logger(os.path.basename(__file__))
comm = MPI.COMM_WORLD
np.seterr(all='raise')
env = Env(comm=comm, config_file=config_file, template_paths=template_paths, hoc_lib_path=hoc_lib_path,
dataset_prefix=dataset_prefix, config_prefix=config_prefix, verbose=verbose)
configure_hoc_env(env)
mech_file_path = config_prefix + '/' + mech_file
template_name = env.celltypes[pop_name]['template']
if template_name.lower() == 'izhikevich':
cell = make_izhikevich_cell(env, pop_name=pop_name, gid=gid,
load_synapses=True, load_connections=True,
load_edges=load_edges, load_weights=load_weights,
mech_file_path=mech_file_path)
elif template_name.lower() == 'pr_nrn':
cell = make_PR_cell(env, pop_name=pop_name, gid=gid,
load_synapses=True, load_connections=True,
load_edges=load_edges, load_weights=load_weights,
mech_file_path=mech_file_path)
else:
cell = make_biophys_cell(env, pop_name=pop_name, gid=gid,
load_synapses=True, load_connections=True,
load_edges=load_edges, load_weights=load_weights,
mech_file_path=mech_file_path)
context.update(locals())
init_biophysics(cell, reset_cable=True, correct_cm=correct_for_spines, correct_g_pas=correct_for_spines,
env=env, verbose=verbose)
init_syn_mech_attrs(cell, env)
config_biophys_cell_syns(env, gid, pop_name, insert=True, insert_netcons=True, insert_vecstims=True,
verbose=verbose)
if verbose:
for sec in list(cell.hoc_cell.all if hasattr(cell, 'hoc_cell') else cell.all):
h.psection(sec=sec)
report_topology(cell, env)
def main(gid, pop_name, config_file, template_paths, hoc_lib_path,
dataset_prefix, config_prefix, mech_file, load_edges, load_weights,
correct_for_spines, verbose):
"""
:param gid: int
:param pop_name: str
:param config_file: str; model configuration file name
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param hoc_lib_path: str; path to directory containing required hoc libraries
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
:param mech_file: str; cell mechanism config file name
:param load_edges: bool; whether to attempt to load connections from a neuroh5 file
:param load_weights: bool; whether to attempt to load connections from a neuroh5 file
:param correct_for_spines: bool
:param verbose: bool
"""
comm = MPI.COMM_WORLD
np.seterr(all='raise')
env = Env(comm=comm,
config_file=config_file,
template_paths=template_paths,
hoc_lib_path=hoc_lib_path,
dataset_prefix=dataset_prefix,
config_prefix=config_prefix,
verbose=verbose)
configure_hoc_env(env)
mech_file_path = config_prefix + '/' + mech_file
cell = get_biophys_cell(env,
pop_name=pop_name,
gid=gid,
load_edges=load_edges,
load_weights=load_weights,
mech_file_path=mech_file_path)
context.update(locals())
init_biophysics(cell,
reset_cable=True,
correct_cm=correct_for_spines,
correct_g_pas=correct_for_spines,
env=env,
verbose=verbose)
init_syn_mech_attrs(cell, env)
config_biophys_cell_syns(env,
gid,
pop_name,
insert=True,
insert_netcons=True,
insert_vecstims=True,
verbose=verbose)
if verbose:
report_topology(cell, env)
def init_biophys_cell(env,
pop_name,
gid,
load_weights=True,
load_connections=True,
register_cell=True,
write_cell=False,
validate_tree=True,
cell_dict={}):
"""
Instantiates a BiophysCell instance and all its synapses.
:param env: an instance of env.Env
:param pop_name: population name
:param gid: gid
:param load_connections: bool
:param register_cell: bool
:param validate_tree: bool
:param write_cell: bool
:param cell_dict: dict
Environment can be instantiated as:
env = Env(config_file, template_paths, dataset_prefix, config_prefix)
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
"""
rank = int(env.pc.id())
## Determine template name for this cell type
template_name = env.celltypes[pop_name]['template']
## Determine if a mechanism configuration file exists for this cell type
if 'mech_file_path' in env.celltypes[pop_name]:
mech_dict = env.celltypes[pop_name]['mech_dict']
else:
mech_dict = None
## Determine if correct_for_spines flag has been specified for this cell type
synapse_config = env.celltypes[pop_name]['synapses']
if 'correct_for_spines' in synapse_config:
correct_for_spines_flag = synapse_config['correct_for_spines']
else:
correct_for_spines_flag = False
## Load cell gid and its synaptic attributes and connection data
if template_name.lower() == 'izhikevich':
cell = cells.make_izhikevich_cell(
env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections)
elif template_name.lower() == 'pr_nrn':
cell = cells.make_PR_cell(env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get(
'connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections)
else:
cell = cells.make_biophys_cell(
env,
pop_name,
gid,
tree_dict=cell_dict.get('morph', None),
synapses_dict=cell_dict.get('synapse', None),
connection_graph=cell_dict.get('connectivity', None),
weight_dict=cell_dict.get('weight', None),
mech_dict=mech_dict,
load_synapses=True,
load_weights=load_weights,
load_edges=load_connections,
validate_tree=validate_tree)
cells.init_biophysics(cell,
reset_cable=True,
correct_cm=correct_for_spines_flag,
correct_g_pas=correct_for_spines_flag,
env=env)
synapses.init_syn_mech_attrs(cell, env)
if register_cell:
cells.register_cell(env, pop_name, gid, cell)
is_reduced = False
if hasattr(cell, 'is_reduced'):
is_reduced = cell.is_reduced
if not is_reduced:
cells.report_topology(cell, env)
env.cell_selection[pop_name] = [gid]
if is_interactive:
context.update(locals())
if write_cell:
write_selection_file_path = "%s/%s_%d.h5" % (env.results_path,
env.modelName, gid)
if rank == 0:
io_utils.mkout(env, write_selection_file_path)
env.comm.barrier()
io_utils.write_cell_selection(env, write_selection_file_path)
if load_connections:
io_utils.write_connection_selection(env, write_selection_file_path)
return cell
def main(config_file, population, gid, template_paths, dataset_prefix,
config_prefix, data_file, load_synapses, syn_types, syn_sources,
syn_source_threshold, font_size, bgcolor, colormap, verbose):
utils.config_logging(verbose)
logger = utils.get_script_logger(script_name)
if dataset_prefix is None and data_file is None:
raise RuntimeError(
'Either --dataset-prefix or --data-file must be provided.')
params = dict(locals())
env = Env(**params)
configure_hoc_env(env)
if env.data_file_path is None:
env.data_file_path = data_file
env.load_celltypes()
## Determine if a mechanism configuration file exists for this cell type
if 'mech_file_path' in env.celltypes[population]:
mech_file_path = env.celltypes[population]['mech_file_path']
else:
mech_file_path = None
## Determine if correct_for_spines flag has been specified for this cell type
synapse_config = env.celltypes[population]['synapses']
if 'correct_for_spines' in synapse_config:
correct_for_spines_flag = synapse_config['correct_for_spines']
else:
correct_for_spines_flag = False
logger.info('loading cell %i' % gid)
load_weights = False
biophys_cell = make_biophys_cell(env,
population,
gid,
load_synapses=load_synapses,
load_weights=load_weights,
load_edges=load_synapses,
mech_file_path=mech_file_path)
init_biophysics(biophys_cell,
reset_cable=True,
correct_cm=correct_for_spines_flag,
correct_g_pas=correct_for_spines_flag,
env=env)
report_topology(biophys_cell, env)
if len(syn_types) == 0:
syn_types = None
else:
syn_types = list(syn_types)
if len(syn_sources) == 0:
syn_sources = None
else:
syn_sources = list(syn_sources)
plot.plot_biophys_cell_tree(env,
biophys_cell,
saveFig=True,
syn_source_threshold=syn_source_threshold,
synapse_filters={
'syn_types': syn_types,
'sources': syn_sources
},
bgcolor=bgcolor,
colormap=colormap)
