def t4():
    print 'Loading Forth XML File (iaf-2coba-Model)'
    print '----------------------------------------'
    component = readers.XMLReader.read_component(Join(tenml_dir,
                                                      'iaf_2coba.10ml'),
                                                 component_name='iaf')
    writers.XMLWriter.write(
        component,
        '/tmp/nineml_toxml4.xml',
    )
    model = readers.XMLReader.read_component(Join(tenml_dir, 'iaf_2coba.10ml'))

    from nineml.abstraction_layer.flattening import flatten
    from nineml.abstraction_layer.dynamics.utils.modifiers import (
        DynamicsModifier)

    flatcomponent = flatten(model, componentname='iaf_2coba')
    DynamicsModifier.close_analog_port(component=flatcomponent,
                                       port_name='iaf_iSyn',
                                       value='0')

    writers.XMLWriter.write(flatcomponent, '/tmp/nineml_out_iaf_2coba.9ml')

    import pyNN.neuron as sim
    from pyNN.utility import init_logging

    init_logging(None, debug=True)
    sim.setup(timestep=0.1, min_delay=0.1)
    print 'Attempting to simulate From Model:'
    print '----------------------------------'
    celltype_cls = pyNNml.nineml_celltype_from_model(
        name="iaf_2coba",
        nineml_model=flatcomponent,
        synapse_components=[
            pyNNml.CoBaSyn(namespace='cobaExcit', weight_connector='q'),
            pyNNml.CoBaSyn(namespace='cobaInhib', weight_connector='q'),
        ])

    parameters = {
        'iaf.cm': 1.0,
        'iaf.gl': 50.0,
        'iaf.taurefrac': 5.0,
        'iaf.vrest': -65.0,
        'iaf.vreset': -65.0,
        'iaf.vthresh': -50.0,
        'cobaExcit.tau': 2.0,
        'cobaInhib.tau': 5.0,
        'cobaExcit.vrev': 0.0,
        'cobaInhib.vrev': -70.0,
    }

    parameters = ComponentFlattener.flatten_namespace_dict(parameters)

    cells = sim.Population(1, celltype_cls, parameters)
    cells.initialize('iaf_V', parameters['iaf_vrest'])
    cells.initialize('tspike', -1e99)  # neuron not refractory at start
    cells.initialize('regime', 1002)  # temporary hack

    input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})

    connector = sim.OneToOneConnector(weights=1.0, delays=0.5)

    conn = [
        sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
        sim.Projection(input[1:2], cells, connector, target='cobaInhib')
    ]

    cells._record('iaf_V')
    cells._record('cobaExcit_g')
    cells._record('cobaInhib_g')
    cells._record('cobaExcit_I')
    cells._record('cobaInhib_I')
    cells.record()

    sim.run(100.0)

    cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
                                   filter=[cells[0]])
    cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc",
                                         filter=[cells[0]])
    cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh",
                                         filter=[cells[0]])
    cells.recorders['cobaExcit_I'].write("Results/nineml_neuron.g_exc",
                                         filter=[cells[0]])
    cells.recorders['cobaInhib_I'].write("Results/nineml_neuron.g_inh",
                                         filter=[cells[0]])

    t = cells.recorders['iaf_V'].get()[:, 1]
    v = cells.recorders['iaf_V'].get()[:, 2]
    gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
    gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
    IInh = cells.recorders['cobaInhib_I'].get()[:, 2]
    IExc = cells.recorders['cobaExcit_I'].get()[:, 2]

    import pylab
    pylab.subplot(311)
    pylab.ylabel('Voltage')
    pylab.plot(t, v)

    pylab.subplot(312)
    pylab.ylabel('Conductance')
    pylab.plot(t, gInh)
    pylab.plot(t, gExc)

    pylab.subplot(313)
    pylab.ylabel('Current')
    pylab.plot(t, IInh)
    pylab.plot(t, IExc)

    pylab.suptitle("From Tree-Model Pathway")
    pylab.show()

    sim.end()
Exemple #2
0
    'iaf.taurefrac': 5.0,
    'iaf.vrest': -65.0,
    'iaf.vreset': -65.0,
    'iaf.vthresh': -50.0,
    # NMDA parameters from Gertner's book, pg 53.
    'nmda.taur': 3.0,  # ms
    'nmda.taud': 40.0,  # ms
    'nmda.gmax': 1.2,  # nS
    'nmda.E': 0.0,
    'nmda.gamma': 0.062,  # 1/mV
    'nmda.mgconc': 1.2,  # mM
    'nmda.beta': 3.57  # mM
}


parameters = ComponentFlattener.flatten_namespace_dict(parameters)


cells = sim.Population(1, celltype_cls, parameters)

cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99)  # neuron not refractory at start
cells.initialize('regime', 1002)  # temporary hack

input = sim.Population(1, sim.SpikeSourcePoisson, {'rate': 100})

connector = sim.OneToOneConnector(weights=1.0, delays=0.5)


conn = [
    sim.Projection(input[0:1], cells, connector, target='nmda'),
def t4():
    print 'Loading Forth XML File (iaf-2coba-Model)'
    print '----------------------------------------'
    component = readers.XMLReader.read_component(
        Join(tenml_dir, 'iaf_2coba.10ml'), component_name='iaf')
    writers.XMLWriter.write(component, '/tmp/nineml_toxml4.xml', )
    model = readers.XMLReader.read_component(Join(tenml_dir, 'iaf_2coba.10ml'))

    from nineml.abstraction_layer.flattening import flatten
    from nineml.abstraction_layer.component_modifiers import ComponentModifier

    flatcomponent = flatten(model, componentname='iaf_2coba')
    ComponentModifier.close_analog_port(component=flatcomponent, port_name='iaf_iSyn', value='0')

    writers.XMLWriter.write(flatcomponent, '/tmp/nineml_out_iaf_2coba.9ml')

    import pyNN.neuron as sim
    from pyNN.utility import init_logging

    init_logging(None, debug=True)
    sim.setup(timestep=0.1, min_delay=0.1)
    print 'Attempting to simulate From Model:'
    print '----------------------------------'
    celltype_cls = pyNNml.nineml_celltype_from_model(
        name="iaf_2coba",
        nineml_model=flatcomponent,
        synapse_components=[
            pyNNml.CoBaSyn(namespace='cobaExcit',  weight_connector='q'),
            pyNNml.CoBaSyn(namespace='cobaInhib',  weight_connector='q'),
        ]
    )

    parameters = {
        'iaf.cm': 1.0,
        'iaf.gl': 50.0,
        'iaf.taurefrac': 5.0,
        'iaf.vrest': -65.0,
        'iaf.vreset': -65.0,
        'iaf.vthresh': -50.0,
        'cobaExcit.tau': 2.0,
        'cobaInhib.tau': 5.0,
        'cobaExcit.vrev': 0.0,
        'cobaInhib.vrev': -70.0,
    }

    parameters = ComponentFlattener.flatten_namespace_dict(parameters)

    cells = sim.Population(1, celltype_cls, parameters)
    cells.initialize('iaf_V', parameters['iaf_vrest'])
    cells.initialize('tspike', -1e99)  # neuron not refractory at start
    cells.initialize('regime', 1002)  # temporary hack

    input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})

    connector = sim.OneToOneConnector(weights=1.0, delays=0.5)

    conn = [sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
            sim.Projection(input[1:2], cells, connector, target='cobaInhib')]

    cells._record('iaf_V')
    cells._record('cobaExcit_g')
    cells._record('cobaInhib_g')
    cells._record('cobaExcit_I')
    cells._record('cobaInhib_I')
    cells.record()

    sim.run(100.0)

    cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
    cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
    cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])
    cells.recorders['cobaExcit_I'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
    cells.recorders['cobaInhib_I'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])

    t = cells.recorders['iaf_V'].get()[:, 1]
    v = cells.recorders['iaf_V'].get()[:, 2]
    gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
    gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
    IInh = cells.recorders['cobaInhib_I'].get()[:, 2]
    IExc = cells.recorders['cobaExcit_I'].get()[:, 2]

    import pylab
    pylab.subplot(311)
    pylab.ylabel('Voltage')
    pylab.plot(t, v)

    pylab.subplot(312)
    pylab.ylabel('Conductance')
    pylab.plot(t, gInh)
    pylab.plot(t, gExc)

    pylab.subplot(313)
    pylab.ylabel('Current')
    pylab.plot(t, IInh)
    pylab.plot(t, IExc)

    pylab.suptitle("From Tree-Model Pathway")
    pylab.show()

    sim.end()
Exemple #4
0
    'iaf.vrest': -65.0,
    'iaf.vreset': -65.0,
    'iaf.vthresh': -50.0,
    'cobaExcit.tau': 2.0,
    'cobaInhib.tau': 5.0,
    'cobaExcit.vrev': 0.0,
    'cobaInhib.vrev': -70.0,
    'cobaExcit.q': 2.0,
    'cobaInhib.q': 2.0,
    'iaf.ISyn':0.0,

    'cobaExcit.gl': 0.0,
}


default_values = ComponentFlattener.flatten_namespace_dict( parameters )



from nestbuilder import NestFileBuilder
nfb = NestFileBuilder(  nest_classname = nest_classname, 
                        component = iaf_cond_exp_9ML_reduced, 
                        synapse_ports = synapse_ports,
                        initial_regime =  initial_regime,
                        initial_values = initial_values,
                        default_values = default_values,
                        )
nfb.compile_files()


Exemple #5
0
def run(plot_and_show=True):
    import sys
    from os.path import abspath, realpath, join
    import numpy
    import nineml

    root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
    sys.path.append(join(root, "lib9ml/python/examples/AL"))
    sys.path.append(join(root, "code_generation/nmodl"))
    sys.path.append(join(root, "code_generation/nest2"))

    #from nineml.abstraction_layer.example_models import  get_hierachical_iaf_3coba
    from nineml.abstraction_layer.testing_utils import TestableComponent
    from nineml.abstraction_layer.flattening import ComponentFlattener

    import pyNN.neuron as sim
    import pyNN.neuron.nineml as pyNNml

    from pyNN.utility import init_logging

    init_logging(None, debug=True)
    sim.setup(timestep=0.1, min_delay=0.1)

    #test_component = get_hierachical_iaf_3coba()
    test_component = TestableComponent('hierachical_iaf_3coba')()

    from nineml.abstraction_layer.writers import DotWriter
    DotWriter.write(test_component, 'test1.dot')

    from nineml.abstraction_layer.writers import XMLWriter
    XMLWriter.write(test_component, 'iaf_3coba.xml')

    celltype_cls = pyNNml.nineml_celltype_from_model(
        name="iaf_3coba",
        nineml_model=test_component,
        synapse_components=[
            pyNNml.CoBaSyn(namespace='AMPA', weight_connector='q'),
            pyNNml.CoBaSyn(namespace='GABAa', weight_connector='q'),
            pyNNml.CoBaSyn(namespace='GABAb', weight_connector='q'),
        ])

    parameters = {
        'iaf.cm': 1.0,
        'iaf.gl': 50.0,
        'iaf.taurefrac': 5.0,
        'iaf.vrest': -65.0,
        'iaf.vreset': -65.0,
        'iaf.vthresh': -50.0,
        'AMPA.tau': 2.0,
        'GABAa.tau': 5.0,
        'GABAb.tau': 50.0,
        'AMPA.vrev': 0.0,
        'GABAa.vrev': -70.0,
        'GABAb.vrev': -95.0,
    }

    parameters = ComponentFlattener.flatten_namespace_dict(parameters)

    cells = sim.Population(1, celltype_cls, parameters)
    cells.initialize('iaf_V', parameters['iaf_vrest'])
    cells.initialize('tspike', -1e99)  # neuron not refractory at start
    cells.initialize('regime', 1002)  # temporary hack

    input = sim.Population(3, sim.SpikeSourceArray)

    numpy.random.seed(12345)
    input[0].spike_times = numpy.add.accumulate(
        numpy.random.exponential(1000.0 / 100.0, size=1000))
    input[1].spike_times = numpy.add.accumulate(
        numpy.random.exponential(1000.0 / 20.0, size=1000))
    input[2].spike_times = numpy.add.accumulate(
        numpy.random.exponential(1000.0 / 50.0, size=1000))

    connector = sim.OneToOneConnector(weights=1.0, delays=0.5)

    conn = [
        sim.Projection(input[0:1], cells, connector, target='AMPA'),
        sim.Projection(input[1:2], cells, connector, target='GABAa'),
        sim.Projection(input[2:3], cells, connector, target='GABAb')
    ]

    cells._record('iaf_V')
    cells._record('AMPA_g')
    cells._record('GABAa_g')
    cells._record('GABAb_g')
    cells.record()

    sim.run(100.0)

    cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
                                   filter=[cells[0]])
    cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc",
                                    filter=[cells[0]])
    cells.recorders['GABAa_g'].write("Results/nineml_neuron.g_gabaA",
                                     filter=[cells[0]])
    cells.recorders['GABAb_g'].write("Results/nineml_neuron.g_gagaB",
                                     filter=[cells[0]])

    t = cells.recorders['iaf_V'].get()[:, 1]
    v = cells.recorders['iaf_V'].get()[:, 2]
    gInhA = cells.recorders['GABAa_g'].get()[:, 2]
    gInhB = cells.recorders['GABAb_g'].get()[:, 2]
    gExc = cells.recorders['AMPA_g'].get()[:, 2]

    if plot_and_show:
        import pylab
        pylab.subplot(211)
        pylab.plot(t, v)
        pylab.ylabel('voltage [mV]')
        pylab.suptitle("AMPA, GABA_A, GABA_B")
        pylab.subplot(212)
        pylab.plot(t, gInhA, label='GABA_A')
        pylab.plot(t, gInhB, label='GABA_B')
        pylab.plot(t, gExc, label='AMPA')
        pylab.ylabel('conductance [nS]')
        pylab.xlabel('t [ms]')
        pylab.legend()

        pylab.show()

    sim.end()
def std_pynn_simulation(test_component, parameters, initial_values,
                        synapse_components, records, plot=True, sim_time=100.,
                        synapse_weights=1.0, syn_input_rate=100):

    from nineml.abstraction_layer.flattening import ComponentFlattener

    import pyNN.neuron as sim
    import pyNN.neuron.nineml as pyNNml
    from pyNN.neuron.nineml import CoBaSyn

    from pyNN.utility import init_logging

    init_logging(None, debug=True)
    sim.setup(timestep=0.01, min_delay=0.1)

    synapse_components_ML = [CoBaSyn(namespace=ns,  weight_connector=wc)
                             for (ns, wc) in synapse_components]

    celltype_cls = pyNNml.nineml_celltype_from_model(
        name=test_component.name,
        nineml_model=test_component,
        synapse_components=synapse_components_ML,
    )

    parameters = ComponentFlattener.flatten_namespace_dict(parameters)
    initial_values = ComponentFlattener.flatten_namespace_dict(initial_values)

    cells = sim.Population(1, celltype_cls, parameters)

    # Set Initial Values:
    for state, state_initial_value in initial_values.iteritems():
        cells.initialize(state, state_initial_value)

    # For each synapse type, create a spike source:
    if synapse_components:
        input = sim.Population(
            len(synapse_components), sim.SpikeSourcePoisson,
            {'rate': syn_input_rate})
        connector = sim.OneToOneConnector(weights=synapse_weights, delays=0.5)

        conn = []
        for i, (ns, weight_connector) in enumerate(synapse_components):
            proj = sim.Projection(input[i:i + 1], cells, connector, target=ns),
            conn.append(proj)

    # Setup the Records:
    for record in records:
        cells.record(record.what)

    cells.record('spikes')

    # Run the simulation:
    sim.run(sim_time)

    if len(records) == 0:
        assert False

    # Write the Results to a file:
    cells.write_data("Results/nineml.pkl")

    # Plot the values:

    results = cells.get_data().segments[0]

    # Create a list of the tags:
    tags = []
    for record in records:
        if not record.tag in tags:
            tags.append(record.tag)

    # Plot the graphs:
    if plot:
        import pylab
        nGraphs = len(tags)

        # Plot the Records:
        for graphIndex, tag in enumerate(tags):
            pylab.subplot(nGraphs, 1, graphIndex + 1)

            for r in records:
                if r.tag != tag:
                    continue
                trace = results.filter(name=r.what)[0]
                pylab.plot(trace.times, trace, label=r.label)

            pylab.ylabel(tag)
            pylab.legend()

        # Plot the spikes:
        # pylab.subplot(nGraphs,1, len(tags)+1)
        # t_spikes = cells[0:1].getSpikes()[:1]
        # pylab.plot( [1,3],[1,3],'x'  )
        # print t_spikes
        # if t_spikes:
        #    pylab.scatter( t_spikes, t_spikes )

        # Add the X axis to the last plot:
        pylab.xlabel('t [ms]')

        # pylab.suptitle("From Tree-Model Pathway")
        pylab.show()

    sim.end()

    return results
def run(plot_and_show=True):
    import sys
    from os.path import abspath, realpath, join
    import numpy
    import nineml

    root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
    sys.path.append(join(root, "lib9ml/python/examples/AL"))
    sys.path.append(join(root, "code_generation/nmodl"))     
    sys.path.append(join(root, "code_generation/nest2"))       
               

    #from nineml.abstraction_layer.example_models import  get_hierachical_iaf_3coba
    from nineml.abstraction_layer.testing_utils import TestableComponent
    from nineml.abstraction_layer.flattening import  ComponentFlattener

    import pyNN.neuron as sim
    import pyNN.neuron.nineml as pyNNml

    from pyNN.utility import init_logging


    init_logging(None, debug=True)
    sim.setup(timestep=0.1, min_delay=0.1)


    #test_component = get_hierachical_iaf_3coba()
    test_component = TestableComponent('hierachical_iaf_3coba')()

    from nineml.abstraction_layer.writers import DotWriter
    DotWriter.write(test_component, 'test1.dot')
    

    from nineml.abstraction_layer.writers import XMLWriter
    XMLWriter.write(test_component, 'iaf_3coba.xml')


    celltype_cls = pyNNml.nineml_celltype_from_model(
                                            name = "iaf_3coba",
                                            nineml_model = test_component,
                                            synapse_components = [
                                                pyNNml.CoBaSyn( namespace='AMPA',  weight_connector='q' ),
                                                pyNNml.CoBaSyn( namespace='GABAa',  weight_connector='q' ),
                                                pyNNml.CoBaSyn( namespace='GABAb',  weight_connector='q' ),
                                                       ]
                                            )

    parameters = {
        'iaf.cm': 1.0,
        'iaf.gl': 50.0,
        'iaf.taurefrac': 5.0,
        'iaf.vrest': -65.0,
        'iaf.vreset': -65.0,
        'iaf.vthresh': -50.0,
        'AMPA.tau': 2.0,
        'GABAa.tau': 5.0,
        'GABAb.tau': 50.0,
        'AMPA.vrev': 0.0,
        'GABAa.vrev': -70.0,
        'GABAb.vrev': -95.0,

    }


    parameters = ComponentFlattener.flatten_namespace_dict( parameters )


    cells = sim.Population(1, celltype_cls, parameters)
    cells.initialize('iaf_V', parameters['iaf_vrest'])
    cells.initialize('tspike', -1e99) # neuron not refractory at start
    cells.initialize('regime', 1002) # temporary hack

    input = sim.Population(3, sim.SpikeSourceArray)

    numpy.random.seed(12345)
    input[0].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/100.0, size=1000))
    input[1].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/20.0, size=1000))
    input[2].spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/50.0, size=1000))

    connector = sim.OneToOneConnector(weights=1.0, delays=0.5)


    conn = [sim.Projection(input[0:1], cells, connector, target='AMPA'),
            sim.Projection(input[1:2], cells, connector, target='GABAa'),
            sim.Projection(input[2:3], cells, connector, target='GABAb')]


    cells._record('iaf_V')
    cells._record('AMPA_g')
    cells._record('GABAa_g')
    cells._record('GABAb_g')
    cells.record()

    sim.run(100.0)

    cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
    cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
    cells.recorders['GABAa_g'].write("Results/nineml_neuron.g_gabaA", filter=[cells[0]])
    cells.recorders['GABAb_g'].write("Results/nineml_neuron.g_gagaB", filter=[cells[0]])


    t = cells.recorders['iaf_V'].get()[:,1]
    v = cells.recorders['iaf_V'].get()[:,2]
    gInhA = cells.recorders['GABAa_g'].get()[:,2]
    gInhB = cells.recorders['GABAb_g'].get()[:,2]
    gExc = cells.recorders['AMPA_g'].get()[:,2]

    if plot_and_show:
        import pylab
        pylab.subplot(211)
        pylab.plot(t,v)
        pylab.ylabel('voltage [mV]')
        pylab.suptitle("AMPA, GABA_A, GABA_B")
        pylab.subplot(212)
        pylab.plot(t,gInhA,label='GABA_A')
        pylab.plot(t,gInhB, label='GABA_B')
        pylab.plot(t,gExc, label='AMPA')
        pylab.ylabel('conductance [nS]')
        pylab.xlabel('t [ms]')
        pylab.legend()

        pylab.show()

    sim.end()
def run(plot_and_show=True):

    import sys
    from os.path import abspath, realpath, join
    import nineml

    root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
    sys.path.append(join(root, "lib9ml/python/examples/AL"))
    sys.path.append(join(root, "code_generation/nmodl"))

    from nineml.abstraction_layer.example_models import get_hierachical_iaf_2coba
    from nineml.abstraction_layer.flattening import ComponentFlattener

    import pyNN.neuron as sim
    import pyNN.neuron.nineml as pyNNml

    from pyNN.utility import init_logging

    init_logging(None, debug=True)
    sim.setup(timestep=0.1, min_delay=0.1)

    testModel = get_hierachical_iaf_2coba()

    celltype_cls = pyNNml.nineml_celltype_from_model(
        name="iaf_2coba",
        nineml_model=testModel,
        synapse_components=[
            pyNNml.CoBaSyn(
                namespace='cobaExcit',  weight_connector='q'),
            pyNNml.CoBaSyn(
                namespace='cobaInhib',  weight_connector='q'),
        ]
    )

    parameters = {
        'iaf.cm': 1.0,
        'iaf.gl': 50.0,
        'iaf.taurefrac': 5.0,
        'iaf.vrest': -65.0,
        'iaf.vreset': -65.0,
        'iaf.vthresh': -50.0,
        'cobaExcit.tau': 2.0,
        'cobaInhib.tau': 5.0,
        'cobaExcit.vrev': 0.0,
        'cobaInhib.vrev': -70.0,
    }

    parameters = ComponentFlattener.flatten_namespace_dict(parameters)

    cells = sim.Population(1, celltype_cls, parameters)
    cells.initialize('iaf_V', parameters['iaf_vrest'])
    cells.initialize('tspike', -1e99)  # neuron not refractory at start
    cells.initialize('regime', 1002)  # temporary hack

    input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})

    connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
    # connector = sim.OneToOneConnector(weights=20.0, delays=0.5)

    conn = [sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
            sim.Projection(input[1:2], cells, connector, target='cobaInhib')]

    cells._record('iaf_V')
    cells._record('cobaExcit_g')
    cells._record('cobaInhib_g')
    cells._record('regime')
    cells.record()

    sim.run(100.0)

    cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
    cells.recorders['regime'].write("Results/nineml_neuron.regime", filter=[cells[0]])
    cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc", filter=[cells[0]])
    cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh", filter=[cells[0]])

    t = cells.recorders['iaf_V'].get()[:, 1]
    v = cells.recorders['iaf_V'].get()[:, 2]
    regime = cells.recorders['regime'].get()[:, 2]
    gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
    gExc = cells.recorders['cobaExcit_g'].get()[:, 2]

    if plot_and_show:
        import pylab
        pylab.subplot(311)
        pylab.plot(t, v)
        pylab.subplot(312)
        pylab.plot(t, gInh)
        pylab.plot(t, gExc)
        pylab.subplot(313)
        pylab.plot(t, regime)
        pylab.ylim((999, 1005))
        pylab.suptitle("From Tree-Model Pathway")
        pylab.show()

    sim.end()
Exemple #9
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parameters = {
    'iaf.cm': 1.0,
    'iaf.gl': 50.0,
    'iaf.taurefrac': 5.0,
    'iaf.vrest': -65.0,
    'iaf.vreset': -65.0,
    'iaf.vthresh': -50.0,
    'cobaExcit.tau': 2.0,
    'cobaInhib.tau': 5.0,
    'cobaExcit.vrev': 0.0,
    'cobaInhib.vrev': -70.0,
    'cobaExcit.q': 2.0,
    'cobaInhib.q': 2.0,
    'iaf.ISyn': 0.0,
    'cobaExcit.gl': 0.0,
}

default_values = ComponentFlattener.flatten_namespace_dict(parameters)

from nestbuilder import NestFileBuilder
nfb = NestFileBuilder(
    nest_classname=nest_classname,
    component=iaf_cond_exp_9ML_reduced,
    synapse_ports=synapse_ports,
    initial_regime=initial_regime,
    initial_values=initial_values,
    default_values=default_values,
)
nfb.compile_files()