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()
import nineml.abstraction_layer as nineml import os #import nineml.models from nineml.abstraction_layer.example_models import get_hierachical_iaf_2coba #from nineml.abstraction_layer.models import reduce_to_single_component, ModelToSingleComponentReducer from nineml.abstraction_layer.flattening import flatten, ComponentFlattener from nineml.abstraction_layer.writers import dump_reduced from nineml.abstraction_layer.componentmodifiers import ComponentModifier nest_classname = "iaf_cond_exp_9ml" iaf_cond_exp_9ML_reduced = flatten( get_hierachical_iaf_2coba(), componentname=nest_classname ) iaf_cond_exp_9ML_reduced.backsub_aliases() iaf_cond_exp_9ML_reduced.backsub_equations() ComponentModifier.close_all_reduce_ports(component=iaf_cond_exp_9ML_reduced) dump_reduced(iaf_cond_exp_9ML_reduced,'reduced.txt') iaf_cond_exp_9ML_reduced.long_description = """ Long description of the iaf_cond_exp ... Author: Eilif Muller, Ecublens, 2011 """ iaf_cond_exp_9ML_reduced.short_description = "Standard integrate and fire with exponential conductance based synapses"
"""
import nineml.abstraction_layer as nineml
import os
#import nineml.models
from nineml.abstraction_layer.example_models import get_hierachical_iaf_2coba
#from nineml.abstraction_layer.models import reduce_to_single_component, ModelToSingleComponentReducer
from nineml.abstraction_layer.flattening import flatten, ComponentFlattener
from nineml.abstraction_layer.writers import dump_reduced
from nineml.abstraction_layer.componentmodifiers import ComponentModifier
nest_classname = "iaf_cond_exp_9ml"
iaf_cond_exp_9ML_reduced = flatten(get_hierachical_iaf_2coba(),
componentname=nest_classname)
iaf_cond_exp_9ML_reduced.backsub_aliases()
iaf_cond_exp_9ML_reduced.backsub_equations()
ComponentModifier.close_all_reduce_ports(component=iaf_cond_exp_9ML_reduced)
dump_reduced(iaf_cond_exp_9ML_reduced, 'reduced.txt')
iaf_cond_exp_9ML_reduced.long_description = """
Long description of the iaf_cond_exp ...
Author: Eilif Muller, Ecublens, 2011
"""
iaf_cond_exp_9ML_reduced.short_description = "Standard integrate and fire with exponential conductance based synapses"
# Things we need to know which should come from the user layer
