def update_run_params(env, param_tuples):
for population in env.biophys_cells:
synapse_config = env.celltypes[population].get('synapses', {})
weights_dict = synapse_config.get('weights', {})
for param_tuple, param_value in param_tuples:
if param_tuple.population != population:
continue
source = param_tuple.source
sec_type = param_tuple.sec_type
syn_name = param_tuple.syn_name
param_path = param_tuple.param_path
if isinstance(param_path, list) or isinstance(param_path, tuple):
p, s = param_path
else:
p, s = param_path, None
sources = None
if isinstance(source, list) or isinstance(source, tuple):
sources = source
else:
if source is not None:
sources = [source]
if isinstance(sec_type, list) or isinstance(sec_type, tuple):
sec_types = sec_type
else:
sec_types = [sec_type]
biophys_cell_dict = env.biophys_cells[population]
for gid in biophys_cell_dict:
biophys_cell = biophys_cell_dict[gid]
is_reduced = False
if hasattr(biophys_cell, 'is_reduced'):
is_reduced = biophys_cell.is_reduced
for this_sec_type in sec_types:
synapses.modify_syn_param(
biophys_cell,
env,
this_sec_type,
syn_name,
param_name=p,
value={s: param_value} if
(s is not None) else param_value,
filters={'sources': sources}
if sources is not None else None,
origin=None if is_reduced else 'soma',
update_targets=True)
def update_network_params(env, param_tuple_values):
if context is None:
raise RuntimeError('update_network: missing required Context object')
for param_tuple, param_value in param_tuple_values:
pop_name = param_tuple.population
biophys_cell_dict = env.biophys_cells[pop_name]
synapse_config = env.celltypes[pop_name]['synapses']
weights_dict = synapse_config.get('weights', {})
for gid in biophys_cell_dict:
biophys_cell = biophys_cell_dict[gid]
is_reduced = False
if hasattr(biophys_cell, 'is_reduced'):
is_reduced = biophys_cell.is_reduced
source = param_tuple.source
sec_type = param_tuple.sec_type
syn_name = param_tuple.syn_name
param_path = param_tuple.param_path
if isinstance(param_path, list) or isinstance(param_path, tuple):
p, s = param_path
else:
p, s = param_path, None
sources = None
if isinstance(source, list) or isinstance(source, tuple):
sources = source
else:
if source is not None:
sources = [source]
if isinstance(sec_type, list) or isinstance(sec_type, tuple):
sec_types = sec_type
else:
sec_types = [sec_type]
for this_sec_type in sec_types:
synapses.modify_syn_param(
biophys_cell,
env,
this_sec_type,
syn_name,
param_name=p,
value={s: param_value} if (s is not None) else param_value,
filters={'sources': sources}
if sources is not None else None,
origin=None if is_reduced else 'soma',
update_targets=True)
def update_network(x, context=None):
"""
:param x: array
:param context: :class:'Context'
"""
if context is None:
raise RuntimeError('update_network: missing required Context object')
for pop_name in context.target_populations:
param_values = context.from_param_vector(x)
for destination, source, sec_type, syn_name, param_path, param_value in param_values:
if pop_name != destination:
continue
conn_params = context.env.connection_config[pop_name][source].mechanisms
sec_type_index = context.env.SWC_Types[sec_type]
if 'default' in conn_params:
mech_params = conn_params['default'][syn_name]
else:
mech_params = conn_params[sec_type_index][syn_name]
if isinstance(param_path, tuple):
p, s = param_path
mech_param = mech_params[p]
try:
mech_param[s] = param_value
except Exception as err:
context.logger.exception('source: %s sec type: %s syn name: %s param path: %s mech params: %s' % (str(source), str(sec_type), str(syn_name), str(param_path), str(mech_params)))
context.logger.exception(err)
raise err
biophys_cell_dict = context.env.biophys_cells[pop_name]
for gid, biophys_cell in viewitems(biophys_cell_dict):
for destination, source, sec_type, syn_name, param_path, param_value in param_values:
if pop_name != destination:
continue
if isinstance(param_path, tuple):
p, s = param_path
else:
p = param_path
synapses.modify_syn_param(biophys_cell, context.env, sec_type, syn_name,
param_name=p, value=param_value,
filters={'sources': [source]},
origin='soma', update_targets=True)
def update_network_clamp(x, context=None):
"""
:param x: array
:param context: :class:'Context'
"""
if context is None:
raise RuntimeError('update_network: missing required Context object')
param_values = context.from_param_vector(x)
context.logger.info("parameter values: %s" % str(param_values))
biophys_cell_dict = context.env.biophys_cells[context.population]
for gid, biophys_cell in viewitems(biophys_cell_dict):
for destination, source, sec_type, syn_name, param_path, param_value in param_values:
if context.population != destination:
continue
if isinstance(param_path, tuple):
p, s = param_path
else:
p, s = param_path, None
sources = None
if isinstance(source, tuple):
sources = list(source)
else:
if source is not None:
sources = [source]
context.logger.info("gid %d: updating parameter %s with value %s" %
(gid, str(p), str(param_value)))
synapses.modify_syn_param(
biophys_cell,
context.env,
sec_type,
syn_name,
param_name=p,
value={s: param_value} if (s is not None) else param_value,
filters={'sources': sources} if sources is not None else None,
origin='soma',
update_targets=True)
context.pop_spike_dict = network_clamp.run(context.env, pc_runworker=False)
def update_network(x, context=None):
"""
:param x: array
:param context: :class:'Context'
"""
if context is None:
raise RuntimeError('update_network: missing required Context object')
x_dict = param_array_to_dict(x, context.param_names)
for postsyn_name in ['GC']:
first_gid = True
for gid in context.env.biophys_cells[postsyn_name]:
if context.comm.rank == 0 and first_gid and context.verbose:
verbose = True
first_gid = False
else:
verbose = False
cell = context.env.biophys_cells[postsyn_name][gid]
for presyn_name, param_name, syn_name, syn_param_name in \
zip(['BC'], ['BC_GC.GABA_A.g_unit'], ['GABA_A'], ['g_unit']):
sec_types = \
context.env.model_config['Connection Generator']['Synapses'][postsyn_name][presyn_name]['sections']
layers = \
context.env.model_config['Connection Generator']['Synapses'][postsyn_name][presyn_name]['layers']
syn_types = \
[context.env.model_config['Connection Generator']['Synapses'][postsyn_name][presyn_name]['type']]
for sec_type in sec_types:
synapses.modify_syn_param(cell,
context.env,
sec_type,
syn_name=syn_name,
param_name=syn_param_name,
filters={
'syn_types': syn_types,
'sources': [presyn_name],
'layers': layers
},
value=x_dict[param_name],
update_targets=True,
verbose=True)
def standard_modify_syn_param_tests(cell, env, syn_name='AMPA', param_name='g_unit', show=False):
"""
:param cell: :class:'BiophysCell'
:param env: :class:'Env'
:param syn_name: str
:param_name: str
"""
start_time = time.time()
gid = cell.gid
pop_name = cell.pop_name
init_syn_mech_attrs(cell, env)
syn_attrs = env.synapse_attributes
sec_type = 'apical'
syn_mech_name = syn_attrs.syn_mech_names[syn_name]
param_label = '%s; %s; %s' % (syn_name, syn_mech_name, 'weight')
plot_synaptic_attribute_distribution(cell, env, syn_name, 'weight', filters=None, from_mech_attrs=True,
from_target_attrs=False, param_label=param_label,
export='syn_weights.hdf5', description='stage0', show=show, overwrite=True,
output_dir=context.output_dir)
config_biophys_cell_syns(env, gid, pop_name, insert=True, insert_netcons=True, insert_vecstims=True,
verbose=context.verbose)
plot_synaptic_attribute_distribution(cell, env, syn_name, 'weight', filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label,
export='syn_weights.hdf5', description='stage1', show=show,
output_dir=context.output_dir)
if param_name in syn_attrs.syn_param_rules[syn_mech_name]['netcon_params']:
param_label = '%s; %s; %s' % (syn_name, syn_mech_name, param_name)
plot_synaptic_attribute_distribution(cell, env, syn_name, param_name, filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label, export='syn_attrs.hdf5',
description='stage0', show=show, overwrite=True,
output_dir=context.output_dir)
modify_syn_param(cell, env, sec_type, syn_name, param_name=param_name, value=0.0005,
filters={'syn_types': ['excitatory']}, origin='soma', slope=0.0001, tau=50., xhalf=200.,
update_targets=True, verbose=context.verbose)
plot_synaptic_attribute_distribution(cell, env, syn_name, param_name, filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label, export='syn_attrs.hdf5',
description='stage1', show=show, output_dir=context.output_dir)
modify_syn_param(cell, env, sec_type, syn_name, param_name=param_name,
filters={'syn_types': ['excitatory'], 'layers': ['OML']}, origin='apical',
origin_filters={'syn_types': ['excitatory'], 'layers': ['MML']}, update_targets=True,
append=True, verbose=context.verbose)
plot_synaptic_attribute_distribution(cell, env, syn_name, param_name, filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label, export='syn_attrs.hdf5',
description='stage2', show=show, output_dir=context.output_dir)
if context.verbose:
print('standard_modify_syn_param tests with cache_queries: %s took %.2f s' % \
(str(env.cache_queries), time.time() - start_time))
plot_syn_attr_from_file(syn_name, param_name, 'syn_attrs.hdf5', param_label=param_label)
else:
param_name = 'weight'
param_label = '%s; %s; %s' % (syn_name, syn_mech_name, param_name)
modify_syn_param(cell, env, sec_type, syn_name, param_name=param_name, value=0.0005,
filters={'syn_types': ['excitatory']}, origin='soma', slope=0.0001, tau=50., xhalf=200.,
update_targets=True, verbose=context.verbose)
plot_synaptic_attribute_distribution(cell, env, syn_name, param_name, filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label, export='syn_weights.hdf5',
description='stage2', show=show, output_dir=context.output_dir)
modify_syn_param(cell, env, sec_type, syn_name, param_name=param_name,
filters={'syn_types': ['excitatory'], 'layers': ['OML']}, origin='apical',
origin_filters={'syn_types': ['excitatory'], 'layers': ['MML']}, update_targets=True,
append=True, verbose=context.verbose)
plot_synaptic_attribute_distribution(cell, env, syn_name, param_name, filters=None, from_mech_attrs=True,
from_target_attrs=True, param_label=param_label, export='syn_weights.hdf5',
description='stage3', show=show, output_dir=context.output_dir)
if context.verbose:
print('standard_modify_syn_param tests with cache_queries: %s took %.2f s' % \
(str(env.cache_queries), time.time() - start_time))
param_name = 'weight'
param_label = '%s; %s; %s' % (syn_name, syn_mech_name, param_name)
plot_syn_attr_from_file(syn_name, param_name, 'syn_weights.hdf5', param_label=param_label)
