def main():
# Let's be very verbose!
logging.basicConfig(level=logging.INFO)
# Let's do multiprocessing this time with a lock (which is default)
filename = os.path.join('hdf5', 'example_23.hdf5')
env = Environment(
trajectory='Example_23_BRIAN2',
filename=filename,
file_title='Example_23_Brian2',
comment='Go Brian2!',
dynamically_imported_classes=[Brian2MonitorResult, Brian2Parameter])
traj = env.trajectory
# 1st a) add the parameters
add_params(traj)
# 1st b) prepare, we want to explore the different network sizes and different tauw time scales
traj.f_explore(
cartesian_product({
traj.f_get('N').v_full_name: [50, 60],
traj.f_get('tauw').v_full_name: [30 * ms, 40 * ms]
}))
# 2nd let's run our experiment
env.run(run_net)
# You can take a look at the results in the hdf5 file if you want!
# Finally disable logging and close all log-files
env.disable_logging()
def main():
# Let's be very verbose!
logging.basicConfig(level = logging.INFO)
# Let's do multiprocessing this time with a lock (which is default)
filename = os.path.join('hdf5', 'example_07.hdf5')
env = Environment(trajectory='Example_07_BRIAN',
filename=filename,
file_title='Example_07_Brian',
comment = 'Go Brian!',
dynamically_imported_classes=[BrianMonitorResult, BrianParameter],
multiproc=True,
wrap_mode='QUEUE',
ncores=2)
traj = env.trajectory
# 1st a) add the parameters
add_params(traj)
# 1st b) prepare, we want to explore the different network sizes and different tauw time scales
traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))
# 2nd let's run our experiment
env.run(run_net)
# You can take a look at the results in the hdf5 file if you want!
# Finally disable logging and close all log-files
env.disable_logging()
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
env = Environment(trajectory='Clustered_Network',
add_time=False,
filename=filename,
continuable=False,
lazy_debug=False,
multiproc=True,
ncores=2,
use_pool=False, # We cannot use a pool, our network cannot be pickled
wrap_mode='QUEUE',
overwrite_file=True)
#Get the trajectory container
traj = env.trajectory
# We introduce a `meta` parameter that we can use to easily rescale our network
scale = 0.5 # To obtain the results from the paper scale this to 1.0
# Be aware that your machine will need a lot of memory then!
traj.f_add_parameter('simulation.scale', scale,
comment='Meta parameter that can scale default settings. '
'Rescales number of neurons and connections strenghts, but '
'not the clustersize.')
# We create a Manager and pass all our components to the Manager.
# Note the order, CNNeuronGroups are scheduled before CNConnections,
# and the Fano Factor computation depends on the CNMonitorAnalysis
clustered_network_manager = NetworkManager(network_runner=CNNetworkRunner(),
component_list=(CNNeuronGroup(), CNConnections()),
analyser_list=(CNMonitorAnalysis(),CNFanoFactorComputer()))
# Add original parameters (but scaled according to `scale`)
clustered_network_manager.add_parameters(traj)
# We need `tolist` here since our parameter is a python float and not a
# numpy float.
explore_list = np.arange(1.0, 2.6, 0.2).tolist()
# Explore different values of `R_ee`
traj.f_explore({'R_ee' : explore_list})
# Pre-build network components
clustered_network_manager.pre_build(traj)
# Run the network simulation
traj.f_store() # Let's store the parameters already before the run
env.run(clustered_network_manager.run_network)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
env = Environment(
trajectory='Clustered_Network',
add_time=False,
filename=filename,
continuable=False,
lazy_debug=False,
multiproc=True,
ncores=2,
use_pool=False, # We cannot use a pool, our network cannot be pickled
wrap_mode='QUEUE',
overwrite_file=True)
#Get the trajectory container
traj = env.trajectory
# We introduce a `meta` parameter that we can use to easily rescale our network
scale = 0.5 # To obtain the results from the paper scale this to 1.0
# Be aware that your machine will need a lot of memory then!
traj.f_add_parameter(
'simulation.scale',
scale,
comment='Meta parameter that can scale default settings. '
'Rescales number of neurons and connections strenghts, but '
'not the clustersize.')
# We create a Manager and pass all our components to the Manager.
# Note the order, CNNeuronGroups are scheduled before CNConnections,
# and the Fano Factor computation depends on the CNMonitorAnalysis
clustered_network_manager = NetworkManager(
network_runner=CNNetworkRunner(),
component_list=(CNNeuronGroup(), CNConnections()),
analyser_list=(CNMonitorAnalysis(), CNFanoFactorComputer()))
# Add original parameters (but scaled according to `scale`)
clustered_network_manager.add_parameters(traj)
# We need `tolist` here since our parameter is a python float and not a
# numpy float.
explore_list = np.arange(1.0, 2.6, 0.2).tolist()
# Explore different values of `R_ee`
traj.f_explore({'R_ee': explore_list})
# Pre-build network components
clustered_network_manager.pre_build(traj)
# Run the network simulation
traj.f_store() # Let's store the parameters already before the run
env.run(clustered_network_manager.run_network)
# Finally disable logging and close all log-files
env.disable_logging()
