# -*- coding: utf-8 -*- """ """ import matplotlib matplotlib.use('Agg') from mpi4py import MPI #from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_cortical_stimulation_luminance_excinh from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD data_store,model = run_workflow('CorticalStimulationModel',SelfSustainedPushPull,create_experiments_cortical_stimulation_luminance_excinh) data_store.save() if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=False,cort_stim=True,nat_stim=False,tp=1)
# -*- coding: utf-8 -*- """ """ import matplotlib matplotlib.use('Agg') from mpi4py import MPI #from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_short,create_experiments_old,create_experiments,create_experiments_tmp from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD if True: data_store,model = run_workflow('CorticalStimulationModel',SelfSustainedPushPull,create_experiments) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'CorticalStimulationModel_visual_stimulation_____base_weight:0.0022_inhibitory_connection_ratio:0.5_layer23_aff_ratio:0.4_stdev:2.7','store_stimuli' : False}),replace=True) if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=True,cort_stim=False,nat_stim=False,tp=0)
""" import matplotlib matplotlib.use('Agg') from mpi4py import MPI from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments,create_experiments_bar,create_experiments_short,create_experiments_old from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD if True: data_store,model = run_workflow('MorganTaylorModel',SelfSustainedPushPull,create_experiments) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'MorganTaylorModel_visual_space_update=1ms_RF_resolution=1ms','store_stimuli' : False}),replace=True) if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=True,bars=True) # data_store.save()
logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop # Model execution if True: # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) if False: # save connections if withPGN: # PGN model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) model.connectors['PGN_PGN_Connection'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store) if withV1: # CORTEX model.connectors['V1AffConnectionOn'].store_connections(data_store) model.connectors['V1AffConnectionOff'].store_connections(data_store) model.connectors['V1AffInhConnectionOn'].store_connections(data_store)
from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from parameters import ParameterSet from model_V1_full import ThalamoCorticalModel from experiments import create_experiments_size try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop withRandomV1conns = False # Model execution data_store, model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size) data_store.save()
The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik from mozaik.controller import run_workflow, setup_logging from experiments import create_experiments from model import KumarEtAl2007 from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow('KumarEtAl2007', KumarEtAl2007, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet( {'root_directory': 'A'}), replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
from model import SSCorrelationConnectivity from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 if True: data_store, model = run_workflow('SSCorrelationConnectivity', SSCorrelationConnectivity, create_experiments) data_store.save() else: setup_logging() data_store = PickledDataStore( load=True, parameters=ParameterSet({ 'root_directory': '/home/jan/cluster/dev/pkg/mozaik/mozaik/contrib/SSCorrelationConn/20140313-113338[param_sd.defaults]CombinationParamSearch{7}/SSCorrelationConnectivity_ParameterSearch_____base_weight:0.00045_sigma:0.5_base_weight:0.0007_rand_struct_ratio:0.5_ExcInhAfferentRatio:1.0_base_weight:0.0007_gain:15.0', 'store_stimuli': False }), replace=True) if mpi_comm.rank == 0: print "Starting visualization"
from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() if True: data_store,model = run_workflow('FFI',PushPullCCModel,create_experiments) model.connectors['V1L4ExcL4ExcConnection'].store_connections(data_store) model.connectors['V1L4ExcL4InhConnection'].store_connections(data_store) model.connectors['V1L4InhL4ExcConnection'].store_connections(data_store) model.connectors['V1L4InhL4InhConnection'].store_connections(data_store) model.connectors['V1AffConnectionOn'].store_connections(data_store) model.connectors['V1AffConnectionOff'].store_connections(data_store) model.connectors['V1AffInhConnectionOn'].store_connections(data_store) model.connectors['V1AffInhConnectionOff'].store_connections(data_store) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'FFI_test_____', 'store_stimuli' : False}),replace=True) logger.info('Loaded data store') data_store.save()
MPI_ROOT = 0 logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = False # open-loop withFeedback_CxPGN = False # closed loop withFeedback_CxLGN = False # closed loop # Model execution if True: # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spontaneous ) data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) if False: # save connections if withPGN: # PGN model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) model.connectors['PGN_PGN_Connection'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store) if withV1: # CORTEX
from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() if True: data_store, model = run_workflow('FFI', PushPullCCModel, create_experiments) #model.connectors['V1L4ExcL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4ExcL4InhConnection'].store_connections(data_store) #model.connectors['V1L4InhL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4InhL4InhConnection'].store_connections(data_store) #model.connectors['V1AffConnection'].store_connections(data_store) #model.connectors['V1AffInhConnection'].store_connections(data_store) else: setup_logging() data_store = PickledDataStore( load=True, parameters=ParameterSet({ 'root_directory': 'FFI_combined-high_resolution_3000_21_____', 'store_stimuli': False }),
Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik from mozaik.controller import run_workflow, setup_logging from experiments import create_experiments from model import KumarEtAl2007 from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow("KumarEtAl2007", KumarEtAl2007, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet({"root_directory": "A"}), replace=True) logger.info("Loaded data store") if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
Journal of physiology, Paris, 101(1-3), 99–109. """ from pyNN import nest import sys from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import Boustani2007 from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow('Boustani2007', Boustani2007, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet( {'root_directory': 'A'}), replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
El Boustani, S., Pospischil, M., Rudolph-Lilith, M., & Destexhe, A. (n.d.). Activated cortical states: experiments, analyses and models. Journal of physiology, Paris, 101(1-3), 99–109. """ from pyNN import nest import sys from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import Boustani2007 from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store,model = run_workflow('Boustani2007',Boustani2007,create_experiments) else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'A'}),replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
# -*- coding: utf-8 -*- """ This is implementation of model of self-sustained activitity in balanced networks from: Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_octc from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization_octc from parameters import ParameterSet from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: data_store,model = run_workflow('SelfSustainedPushPull',SelfSustainedPushPull,create_experiments_octc) data_store.save() if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization_octc(data_store) data_store.save()
import sys from pyNN import nest import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import SSCorrelationConnectivity from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 if True: data_store,model = run_workflow('SSCorrelationConnectivity',SSCorrelationConnectivity,create_experiments) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'/home/jan/cluster/dev/pkg/mozaik/mozaik/contrib/SSCorrelationConn/20140313-113338[param_sd.defaults]CombinationParamSearch{7}/SSCorrelationConnectivity_ParameterSearch_____base_weight:0.00045_sigma:0.5_base_weight:0.0007_rand_struct_ratio:0.5_ExcInhAfferentRatio:1.0_base_weight:0.0007_gain:15.0', 'store_stimuli' : False}),replace=True) if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) # data_store.save()
MPI_ROOT = 0 from model import PushPullCCModel from experiments import create_experiments from parameters import ParameterSet import mozaik from mozaik.controller import run_workflow, setup_logging from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore print mozaik.__file__ print sys.path logger = mozaik.getMozaikLogger() if True: data_store, model = run_workflow('FeedForwardInhibition', PushPullCCModel, create_experiments) #model.connectors['V1L4ExcL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4ExcL4InhConnection'].store_connections(data_store) #model.connectors['V1L4InhL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4InhL4InhConnection'].store_connections(data_store) #model.connectors['V1AffConnectionOn'].store_connections(data_store) #model.connectors['V1AffConnectionOff'].store_connections(data_store) #model.connectors['V1AffInhConnectionOn'].store_connections(data_store) #model.connectors['V1AffInhConnectionOff'].store_connections(data_store) data_store.save() if mpi_comm.rank == MPI_ROOT: from analysis_and_visualization import perform_analysis_and_visualization perform_analysis_and_visualization(data_store) else: setup_logging()
# Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = False # closed loop withFeedback_CxLGN = False # closed loop # Model execution if False: # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spontaneous ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) data_store, model = run_workflow("ThalamoCorticalModel", ThalamoCorticalModel, create_experiments_size) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) if False: # save connections if withPGN: # PGN model.connectors["LGN_PGN_ConnectionOn"].store_connections(data_store) model.connectors["LGN_PGN_ConnectionOff"].store_connections(data_store) model.connectors["PGN_PGN_Connection"].store_connections(data_store) model.connectors["PGN_LGN_ConnectionOn"].store_connections(data_store) model.connectors["PGN_LGN_ConnectionOff"].store_connections(data_store) # if withV1: # CORTEX # # model.connectors['V1AffConnectionOn'].store_connections(data_store) # # model.connectors['V1AffConnectionOff'].store_connections(data_store) # # model.connectors['V1AffInhConnectionOn'].store_connections(data_store) # # model.connectors['V1AffInhConnectionOff'].store_connections(data_store)
# -*- coding: utf-8 -*- """ """ import matplotlib matplotlib.use('Agg') from mpi4py import MPI #from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_cortical_stimulation_or_exc_LumBased from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD data_store,model = run_workflow('CorticalStimulationModel',SelfSustainedPushPull,create_experiments_cortical_stimulation_or_exc_LumBased) data_store.save() if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=False,cort_stim=True,nat_stim=False,tp=1)
# -*- coding: utf-8 -*- """ """ #from mpi4py import MPI from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_cs,create_experiments_bar from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization_bar,perform_analysis_and_visualization_contrast_sensitivity,perform_analysis_and_visualization_small from parameters import ParameterSet #mpi_comm = MPI.COMM_WORLD if True: data_store,model = run_workflow('TestLGN',SelfSustainedPushPull,create_experiments_bar) data_store.save() else: data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'TestLGN_rup=28;rfr=7_____','store_stimuli' : False}),replace=True) #if mpi_comm.rank == 0: # print "Starting visualization" perform_analysis_and_visualization_small(data_store)
""" import matplotlib matplotlib.use('Agg') from mpi4py import MPI #from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_cortical_stimulation_or_exc_LumBased from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD data_store, model = run_workflow( 'CorticalStimulationModel', SelfSustainedPushPull, create_experiments_cortical_stimulation_or_exc_LumBased) data_store.save() if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store, gratings=False, cort_stim=True, nat_stim=False, tp=1)
mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop # Model execution if True: data_store, model = run_workflow( 'ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size_V1_inactivated_nonoverlapping) data_store.save() # or only load pickled data else: setup_logging() # data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'Deliverable/ThalamoCorticalModel_data_size_nonoverlapping_____', 'store_stimuli' : False}),replace=True) data_store = PickledDataStore( load=True, parameters=ParameterSet({ 'root_directory': 'ThalamoCorticalModel_data_size_nonoverlapping_____', 'store_stimuli': False }), replace=True)
from mpi4py import MPI from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_cs, create_experiments_bar from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization_bar, perform_analysis_and_visualization_contrast_sensitivity, perform_analysis_and_visualization_small from parameters import ParameterSet #mpi_comm = MPI.COMM_WORLD if True: data_store, model = run_workflow('TestLGN', SelfSustainedPushPull, create_experiments_cs) data_store.save() else: data_store = PickledDataStore(load=True, parameters=ParameterSet({ 'root_directory': 'TestLGN_test_____', 'store_stimuli': False }), replace=True) #if mpi_comm.rank == 0: # print "Starting visualization" perform_analysis_and_visualization_contrast_sensitivity(data_store)
from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() print sys.argv if False: data_store, model = run_workflow('T05', T05_Model, create_experiments) model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) model.connectors['PGN_PGN_Connection'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet({ 'store_stimuli': False, 'root_directory': 'T05_data_____' }),
logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = False # open-loop withFeedback_CxPGN = False # closed loop withFeedback_CxLGN = False # closed loop # Model execution if True: # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spontaneous ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) if False: # save connections if withPGN: # PGN model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) model.connectors['PGN_PGN_Connection'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store) if withV1: # CORTEX model.connectors['V1AffConnectionOn'].store_connections(data_store)
from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import VogelsAbbottPushPullFixedK from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow('VogeslAbbott2005PushPullFixedK', VogelsAbbottPushPullFixedK, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet( {'root_directory': 'A'}), replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
# Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop # Model execution if True: # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spontaneous ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size ) data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size_V1_inactivated_overlapping ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) # if False: # save connections # if withPGN: # PGN # model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) # model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) # model.connectors['PGN_PGN_Connection'].store_connections(data_store) # model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) # model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store) # if withV1: # CORTEX # # model.connectors['V1AffConnectionOn'].store_connections(data_store) # # model.connectors['V1AffConnectionOff'].store_connections(data_store) # # model.connectors['V1AffInhConnectionOn'].store_connections(data_store) # # model.connectors['V1AffInhConnectionOff'].store_connections(data_store)
Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import VogelsAbbottPushPull from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow("VogeslAbbott2005PushPull", VogelsAbbottPushPull, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet({"root_directory": "A"}), replace=True) logger.info("Loaded data store") if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
# -*- coding: utf-8 -*- """ """ import matplotlib matplotlib.use('Agg') from mpi4py import MPI #from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_short,create_experiments_old,create_experiments,create_experiments_tmp from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD if True: data_store,model = run_workflow('CorticalStimulationModel',SelfSustainedPushPull,create_experiments_tmp) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'CorticalStimulationModel_visual_stimulation_____base_weight:0.0022_inhibitory_connection_ratio:0.5_layer23_aff_ratio:0.4_stdev:2.7','store_stimuli' : False}),replace=True) if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=True,cort_stim=False,nat_stim=False,tp=0)
import sys import mozaik from mozaik.controller import run_workflow, setup_logging from experiments import create_experiments from model import VogelsAbbott from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow("VogeslAbbott2005", VogelsAbbott, create_experiments) print model.connectors model.connectors["V1L4ExcL4ExcConnection"].store_connections(data_store) model.connectors["V1L4ExcL4InhConnection"].store_connections(data_store) model.connectors["V1L4InhL4ExcConnection"].store_connections(data_store) model.connectors["V1L4InhL4InhConnection"].store_connections(data_store) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet({"root_directory": "A"}), replace=True) logger.info("Loaded data store") if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() print sys.argv if True: data_store,model = run_workflow( 'T0', T0_Model, create_experiments ) else: setup_logging() data_store = PickledDataStore( load=True, parameters=ParameterSet({ 'store_stimuli':False, 'root_directory':'T0_data_____' }) ,replace=True ) logger.info('Loaded data store') if mpi_comm.rank == MPI_ROOT: perform_analysis_and_visualization(data_store)
from parameters import ParameterSet import mozaik from mozaik.controller import run_workflow, setup_logging from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore print mozaik.__file__ print sys.path logger = mozaik.getMozaikLogger() if True: data_store,model = run_workflow('FeedForwardInhibition',PushPullCCModel,create_experiments) #model.connectors['V1L4ExcL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4ExcL4InhConnection'].store_connections(data_store) #model.connectors['V1L4InhL4ExcConnection'].store_connections(data_store) #model.connectors['V1L4InhL4InhConnection'].store_connections(data_store) #model.connectors['V1AffConnectionOn'].store_connections(data_store) #model.connectors['V1AffConnectionOff'].store_connections(data_store) #model.connectors['V1AffInhConnectionOn'].store_connections(data_store) #model.connectors['V1AffInhConnectionOff'].store_connections(data_store) data_store.save() if mpi_comm.rank == MPI_ROOT: from analysis_and_visualization import perform_analysis_and_visualization perform_analysis_and_visualization(data_store) else: setup_logging()
from analysis_and_visualization import perform_analysis_and_visualization from model import SelfSustainedPushPull from experiments import create_experiments_RF_estimation import mozaik from mozaik.controller import run_workflow, setup_logging import mozaik.controller import sys from pyNN import nest import matplotlib matplotlib.use('Agg') mpi_comm = MPI.COMM_WORLD if True: data_store, model = run_workflow('SelfSustainedPushPull', SelfSustainedPushPull, create_experiments_RF_estimation) if False: model.connectors['V1AffConnectionOn'].store_connections(data_store) model.connectors['V1AffConnectionOff'].store_connections(data_store) model.connectors['V1AffInhConnectionOn'].store_connections(data_store) model.connectors['V1AffInhConnectionOff'].store_connections(data_store) model.connectors['V1L4ExcL4ExcConnection'].store_connections( data_store) model.connectors['V1L4ExcL4InhConnection'].store_connections( data_store) model.connectors['V1L4InhL4ExcConnection'].store_connections( data_store) model.connectors['V1L4InhL4InhConnection'].store_connections( data_store) model.connectors['V1L4ExcL4ExcConnectionRand'].store_connections(
# -*- coding: utf-8 -*- import matplotlib matplotlib.use("Agg") from model import SelfSustainedPushPull from experiments import create_experiments import mozaik from mozaik.controller import run_workflow data_store, model = run_workflow("LSV1M", SelfSustainedPushPull, create_experiments) data_store.save()
# -*- coding: utf-8 -*- """ """ import matplotlib matplotlib.use('Agg') from mpi4py import MPI from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments,create_experiments_bar,create_experiments_short,create_experiments_old,create_experiments_old_short from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD data_store,model = run_workflow('MorganTaylorModel',SelfSustainedPushPull,create_experiments_old) data_store.save() if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store,gratings=True,bars=False,nat_movies=True)
from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() if True: data_store, model = run_workflow('TestStimuliModel', TestStimuliModel, create_experiments) data_store.save() else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet({ 'root_directory': 'TestStimuliModel_test_____', 'store_stimuli': False }), replace=True) logger.info('Loaded data store') data_store.save()
logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # # withV1 = False # open-loop # withFeedback_CxPGN = False # closed loop # withFeedback_CxLGN = False # closed loop withV1 = True # open-loop withFeedback_CxPGN = True # closed loop withFeedback_CxLGN = True # closed loop # Model execution if False: data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spontaneous ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_luminance ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_contrast ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_spatial ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_temporal ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_size ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_orientation ) # data_store,model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_experiments_combined ) if True: # save connections # if withPGN: # PGN # model.connectors['LGN_PGN_ConnectionOn'].store_connections(data_store) # model.connectors['LGN_PGN_ConnectionOff'].store_connections(data_store) # model.connectors['PGN_PGN_Connection'].store_connections(data_store) # model.connectors['PGN_LGN_ConnectionOn'].store_connections(data_store) # model.connectors['PGN_LGN_ConnectionOff'].store_connections(data_store)
Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import VogelsAbbottPushPullFixedK from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store,model = run_workflow('VogeslAbbott2005PushPullFixedK',VogelsAbbottPushPullFixedK,create_experiments) else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'A'}),replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik from mozaik.controller import run_workflow, setup_logging from experiments import create_experiments from model import VogelsAbbott from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store, model = run_workflow('VogeslAbbott2005', VogelsAbbott, create_experiments) else: setup_logging() data_store = PickledDataStore(load=True, parameters=ParameterSet( {'root_directory': 'A'}), replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
mpirun python run.py simulator_name number_processors parameters name_of_test For example: mpirun python run.py nest 2 param/defaults 'test' """ from mpi4py import MPI from pyNN import nest import sys import mozaik from mozaik.controller import run_workflow, setup_logging from experiments import create_experiments from model import VogelsAbbott from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet mpi_comm = MPI.COMM_WORLD logger = mozaik.getMozaikLogger() if True: data_store,model = run_workflow('VogelsAbbott2005',VogelsAbbott,create_experiments) else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'VogelsAbbott2005_test_____', 'store_stimuli' : False}),replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()
from mozaik.cli import parse_workflow_args from mozaik.controller import run_workflow, setup_logging from mozaik.tools.misc import result_directory_name from experiments import create_experiments from model import VogelsAbbott from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from parameters import ParameterSet #mpi_comm = MPI.COMM_WORLD logger = mozaik.getMozaikLogger() simulation_name = "VogelsAbbott2005" simulation_run_name, _, _, _, modified_parameters = parse_workflow_args() if True: data_store,model = run_workflow(simulation_name,VogelsAbbott,create_experiments) model.connectors['ExcExcConnection'].store_connections(data_store) else: setup_logging() data_store = PickledDataStore( load=True, parameters=ParameterSet( { "root_directory": result_directory_name( simulation_run_name, simulation_name, modified_parameters ), "store_stimuli": False, } ), replace=True, )
from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from parameters import ParameterSet from model_feedforward import ThalamoCorticalModel from experiments import create_interrupted_bar try: from mpi4py import MPI except ImportError: MPI = None if MPI: mpi_comm = MPI.COMM_WORLD MPI_ROOT = 0 logger = mozaik.getMozaikLogger() # Manage what is executed # a set of variable here to manage the type of experiment and whether the pgn, cortex are there or not. withPGN = True # withV1 = True # open-loop withFeedback_CxPGN = False # ffw loop withFeedback_CxLGN = False # ffw loop withRandomV1conns = False # Model execution data_store, model = run_workflow('ThalamoCorticalModel', ThalamoCorticalModel, create_interrupted_bar) data_store.save()
# -*- coding: utf-8 -*- """ This is implementation of model of self-sustained activitity in balanced networks from: Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments_spont from model import SelfSustainedPushPull from mozaik.storage.datastore import Hdf5DataStore, PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization_spont from parameters import ParameterSet from mpi4py import MPI mpi_comm = MPI.COMM_WORLD data_store, model = run_workflow('SelfSustainedPushPull', SelfSustainedPushPull, create_experiments_spont) data_store.save() perform_analysis_and_visualization_spont(data_store)
Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience, 25(46), 10786–95. """ from pyNN import nest import sys import mozaik.controller from mozaik.controller import run_workflow, setup_logging import mozaik from experiments import create_experiments from model import VogelsAbbottPushPullFixedKBackgroundCurrent from mozaik.storage.datastore import Hdf5DataStore,PickledDataStore from analysis_and_visualization import perform_analysis_and_visualization from mpi4py import MPI mpi_comm = MPI.COMM_WORLD if True: logger = mozaik.getMozaikLogger() data_store,model = run_workflow('VogeslAbbott2005PushPullFixedKBackgroundCurrent',VogelsAbbottPushPullFixedKBackgroundCurrent,create_experiments) else: setup_logging() data_store = PickledDataStore(load=True,parameters=ParameterSet({'root_directory':'A'}),replace=True) logger.info('Loaded data store') if mpi_comm.rank == 0: print "Starting visualization" perform_analysis_and_visualization(data_store) data_store.save()