def _get_lems_model_with_neuroml2_types(cls):
from pyneuroml.pynml import get_path_to_jnml_jar
from pyneuroml.pynml import read_lems_file
from lems.parser.LEMS import LEMSFileParser
import zipfile
lems_model = lems.Model(include_includes=False)
parser = LEMSFileParser(lems_model)
jar_path = get_path_to_jnml_jar()
# print_comment_v("Loading standard NeuroML2 dimension/unit definitions from %s"%jar_path)
jar = zipfile.ZipFile(jar_path, "r")
new_lems = jar.read("NeuroML2CoreTypes/NeuroMLCoreDimensions.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/NeuroMLCoreCompTypes.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/Cells.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/Networks.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/Simulation.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/Synapses.xml")
parser.parse(new_lems)
new_lems = jar.read("NeuroML2CoreTypes/PyNN.xml")
parser.parse(new_lems)
return lems_model
def read_lems_file(fname):
"""Import LEMS model from file fname or builtin model name.
"""
here = os.path.dirname(os.path.abspath(__file__))
builtin_fname = os.path.join(here, fname + '.lems.xml')
if os.path.exists(builtin_fname):
fname = builtin_fname
model = lems.Model()
model.import_from_file(fname)
return model
def __init__(self):
self._model = lems.Model()
self._all_params_unit = {}
self._population = None
self._model_namespace = {'neuronname': None,
'ct_populationname': None,
'populationname': None,
'networkname': None,
'targetname': None,
'simulname': None,
'poiss_generator': "poiss"}
def _load_lems_file_with_neuroml2_types(cls, lems_filename):
from pyneuroml.pynml import get_path_to_jnml_jar
from pyneuroml.pynml import read_lems_file
from lems.parser.LEMS import LEMSFileParser
import zipfile
lems_model = lems.Model(include_includes=False)
parser = LEMSFileParser(lems_model)
jar_path = get_path_to_jnml_jar()
# print_comment_v("Loading standard NeuroML2 dimension/unit definitions from %s"%jar_path)
jar = zipfile.ZipFile(jar_path, 'r')
new_lems = jar.read('NeuroML2CoreTypes/NeuroMLCoreDimensions.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/NeuroMLCoreCompTypes.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/Cells.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/Networks.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/Simulation.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/Synapses.xml')
parser.parse(new_lems)
new_lems = jar.read('NeuroML2CoreTypes/PyNN.xml')
parser.parse(new_lems)
model = read_lems_file(lems_filename,
include_includes=False,
fail_on_missing_includes=True,
debug=True)
for cid, c in model.components.items():
lems_model.components[cid] = c
for ctid, ct in model.component_types.items():
lems_model.component_types[ctid] = ct
return lems_model
def build_lems_for_model(src):
model = lems.Model()
model.add(lems.Dimension('time', t=1))
# model.add(lems.Dimension('au'))
# primary element of the model is a mass model component
mass = lems.ComponentType(src.name, extends="baseCellMembPot")
model.add(mass)
######### Adding v is required to ease mapping to NEURON...
mass.dynamics.add(lems.StateVariable(name="v", dimension="voltage", exposure="v"))
mass.add(lems.Attachments(name="synapses",type_="basePointCurrentDL"))
for input in src.input:
mass.dynamics.add(lems.DerivedVariable(name=input,
dimension='none',
exposure=input,
select='synapses[*]/I',
reduce='add'))
mass.add(lems.Exposure(input, 'none'))
for key, val in src.const.items():
mass.add(lems.Parameter(key, 'none')) # TODO units
mass.add(lems.Constant(name="MSEC", dimension="time", value="1ms"))
mass.add(lems.Constant(name="PI", dimension="none", value="3.14159265359"))
states = []
der_vars = []
# for key in src.param:
# mass.add(lems.Parameter(key, 'au')) # TODO units
for key, val in src.auxex:
val = val.replace('**', '^')
mass.dynamics.add(lems.DerivedVariable(key, value=val))
for key in src.obsrv:
name_dv = key.replace('(','_').replace(')','').replace(' - ','_min_')
mass.dynamics.add(lems.DerivedVariable(name_dv, value=key, exposure=name_dv))
mass.add(lems.Exposure(name_dv, 'none'))
for src_svar in src.state_space:
name = src_svar.name
ddt = src_svar.drift.replace('**', '^')
mass.dynamics.add(lems.StateVariable(name, 'none', name))
mass.dynamics.add(lems.TimeDerivative(name, '(%s)/MSEC'%ddt))
mass.add(lems.Exposure(name, 'none'))
''' On condition is not need on the model but NeuroML requires its definition -->
<OnCondition test="r .lt. 0">
<EventOut port="spike"/>
</OnCondition>'''
oc = lems.OnCondition(test='v .gt. 0')
oc.actions.append(lems.EventOut(port='spike'))
mass.dynamics.add(oc)
return model
def handle_population(self,
population_id,
component, size=-1,
component_obj=None,
properties={}):
sizeInfo = " as yet unspecified size"
if size>=0:
sizeInfo = ", size: "+ str(size)+ " cells"
if component_obj:
compInfo = " (%s)"%component_obj.__class__.__name__
else:
compInfo=""
print_v("Population: "+population_id+", component: "+component+compInfo+sizeInfo)
if size>=0:
for i in range(size):
node_id = '%s_%i'%(population_id, i)
node = {}
node['type'] = {}
node['name'] = node_id
#node['type']['NeuroML'] = component
comp = self.nl_network.get_child(component, 'cells')
base_dir = './' # for now...
fname = locate_file(comp.lems_source_file, base_dir)
model = lems.Model()
model.import_from_file(fname)
lems_comp = model.components.get(component)
print('Cell: [%s] comes from %s and in Lems is: %s'%(comp,fname, lems_comp))
comp_type = lems_comp.type
type = "The type of %s"%comp_type
function = "The function of %s"%comp_type
if comp_type == 'pnlLinearFunctionTM':
function = 'Linear'
type = "TransferMechanism"
elif comp_type == 'inputNode':
function = 'Linear'
type = "TransferMechanism"
elif comp_type == 'pnlLogisticFunctionTM':
function = 'Logistic'
type = "TransferMechanism"
elif comp_type == 'pnlExponentialFunctionTM':
function = 'Exponential'
type = "TransferMechanism"
elif comp_type == 'pnlSimpleIntegratorMechanism':
function = 'SimpleIntegrator'
type = "IntegratorMechanism"
node['type']["PNL"] = type
node['type']["generic"] = None
node['parameters'] = {}
node['parameters']['PNL'] = {}
node['functions'] = []
func_info = {}
func_info['type']={}
func_info['type']['generic']=function
func_info['name']='Function_%s'%function
func_info['args']={}
for p in lems_comp.parameters:
func_info['args'][p] = {}
func_info['args'][p]['type'] = 'float'
func_info['args'][p]['source'] = '%s.input_ports.%s'%(node_id,p)
if comp.parameters is not None and p in comp.parameters:
func_info['args'][p]['value'] = evaluate(comp.parameters[p], self.nl_network.parameters)
else:
func_info['args'][p]['value'] = evaluate(lems_comp.parameters[p]) # evaluate to ensure strings -> ints/floats etc
node['functions'].append(func_info)
self.bids_mdf_graph['nodes'][node_id] = node
pop_node_id = '%s'%(population_id)
pop_node = {}
pop_node['type'] = {}
pop_node['name'] = pop_node_id
pop_node['type']['NeuroML'] = component
pop_node['parameters'] = {}
pop_node['parameters']['size'] = size
pop_node['functions'] = {}
self.bids_mdf_graph_hl['nodes'][pop_node_id] = pop_node
#! /usr/bin/python
import lems.api as lems
model = lems.Model()
model.add(lems.Dimension('voltage', m=1, l=3, t=-3, i=-1))
model.add(lems.Dimension('time', t=1))
model.add(lems.Dimension('capacitance', m=-1, l=-2, t=4, i=2))
model.add(lems.Unit('milliVolt', 'mV', 'voltage', -3))
model.add(lems.Unit('milliSecond', 'ms', 'time', -3))
model.add(lems.Unit('microFarad', 'uF', 'capacitance', -12))
iaf1 = lems.ComponentType('iaf1')
model.add(iaf1)
iaf1.add(lems.Parameter('threshold', 'voltage'))
iaf1.add(lems.Parameter('reset', 'voltage'))
iaf1.add(lems.Parameter('refractoryPeriod', 'time'))
iaf1.add(lems.Parameter('capacitance', 'capacitance'))
iaf1.add(lems.Exposure('vexp', 'voltage'))
dp = lems.DerivedParameter('range', 'threshold - reset', 'voltage')
iaf1.add(dp)
iaf1.dynamics.add(lems.StateVariable('v','voltage', 'vexp'))
iaf1.dynamics.add(lems.DerivedVariable('v2',dimension='voltage', value='v*2'))
cdv = lems.ConditionalDerivedVariable('v_abs','voltage')
cdv.add(lems.Case('v .geq. 0','v'))
cdv.add(lems.Case('v .lt. 0','-1*v'))
iaf1.dynamics.add(cdv)
def create_GoC_network( duration, dt, seed, N_goc=0, run=False, prob_type='Boltzmann', GJw_type='Vervaeke2010' ):
goc_filename = 'GoC.cell.nml'
goc_file = pynml.read_neuroml2_file( goc_filename )
goc_type = goc_file.cells[0]
GJ_filename = 'GapJuncCML.nml'
GJ_file = pynml.read_neuroml2_file( GJ_filename )
GJ_type = GJ_file.gap_junctions[0]
MFSyn_filename = 'MF_GoC_Syn.nml'
mfsyn_file = pynml.read_neuroml2_file( MFSyn_filename )
MFSyn_type = mfsyn_file.exp_three_synapses[0]
MF20Syn_filename = 'MF_GoC_SynMult.nml'
mf20syn_file = pynml.read_neuroml2_file( MF20Syn_filename )
MF20Syn_type = mf20syn_file.exp_three_synapses[0]
# Distribute cells in 3D
if N_goc>0:
GoC_pos = nu.GoC_locate(N_goc)
else:
GoC_pos = nu.GoC_density_locate()
N_goc = GoC_pos.shape[0]
# get GJ connectivity
GJ_pairs, GJWt = nu.GJ_conn( GoC_pos, prob_type, GJw_type )
tmp1, tmp2 = valnet.gapJuncAnalysis( GJ_pairs, GJWt )
print("Number of gap junctions per cell: ", tmp1)
print("Net GJ conductance per cell:", tmp2)
# Create pop List
goc_pop = nml.Population( id=goc_type.id+"Pop", component = goc_type.id, type="populationList", size=N_goc )
# Create NML document for network specification
net = nml.Network( id="gocNetwork", type="networkWithTemperature" , temperature="23 degC" )
net_doc = nml.NeuroMLDocument( id=net.id )
net_doc.networks.append( net )
net_doc.includes.append( goc_type )
net.populations.append( goc_pop )
#Add locations for GoC instances in the population:
for goc in range(N_goc):
inst = nml.Instance( id=goc )
goc_pop.instances.append( inst )
inst.location = nml.Location( x=GoC_pos[goc,0], y=GoC_pos[goc,1], z=GoC_pos[goc,2] )
# Define input spiketrains
input_type = 'spikeGenerator'#'spikeGeneratorPoisson'
lems_inst_doc = lems.Model()
mf_inputs = lems.Component( "MF_Input", input_type)
mf_inputs.set_parameter("period", "2000 ms" )
#mf_inputs.set_parameter("averageRate", "50 Hz")
lems_inst_doc.add( mf_inputs )
#synapse_type = 'alphaCurrentSynapse'
#alpha_syn = lems.Component( "AlphaSyn", synapse_type)
#alpha_syn.set_parameter("tau", "30 ms" )
#alpha_syn.set_parameter("ibase", "200 pA")
#lems_inst_doc.add( alpha_syn )
# Define MF input population
N_mf = 15
#MF_pop = nml.Population(id=mf_inputs.id+"_pop", component=mf_inputs.id, type="populationList", size=N_mf)
#net.populations.append( MF_pop )
mf_type2 = 'spikeGeneratorPoisson'
#mf_poisson = lems.Component( "MF_Poisson", mf_type2)
#mf_poisson.set_parameter("averageRate", "5 Hz")
#lems_inst_doc.add( mf_poisson )
# adding in neuroml document instead of mf_poisson
mf_poisson = nml.SpikeGeneratorPoisson( id = "MF_Poisson", average_rate="5 Hz" )
net_doc.spike_generator_poissons.append( mf_poisson )
net_doc.includes.append( goc_type )
MF_Poisson_pop = nml.Population(id=mf_poisson.id+"_pop", component=mf_poisson.id, type="populationList", size=N_mf)
net.populations.append( MF_Poisson_pop )
MF_pos = nu.GoC_locate( N_mf )
for mf in range( N_mf ):
inst = nml.Instance(id=mf)
MF_Poisson_pop.instances.append( inst )
inst.location = nml.Location( x=MF_pos[mf,0], y=MF_pos[mf,1], z=MF_pos[mf,2] )
# Setup Mf->GoC synapses
#MFprojection = nml.Projection(id="MFtoGoC", presynaptic_population=MF_pop.id, postsynaptic_population=goc_pop.id, synapse=alpha_syn.id)
#net.projections.append(MFprojection)
MF2projection = nml.Projection(id="MF2toGoC", presynaptic_population=MF_Poisson_pop.id, postsynaptic_population=goc_pop.id, synapse=MFSyn_type.id)#alpha_syn.id
net.projections.append(MF2projection)
#Get list of MF->GoC synapse
mf_synlist = nu.randdist_MF_syn( N_mf, N_goc, pConn=0.3)
nMFSyn = mf_synlist.shape[1]
for syn in range( nMFSyn ):
mf, goc = mf_synlist[:, syn]
conn2 = nml.Connection(id=syn, pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, mf, mf_poisson.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
MF2projection.connections.append(conn2)
# Burst of MF input (as explicit input)
mf_bursttype = 'transientPoissonFiringSynapse'
mf_burst = lems.Component( "MF_Burst", mf_bursttype)
mf_burst.set_parameter( "averageRate", "100 Hz" )
mf_burst.set_parameter( "delay", "2000 ms" )
mf_burst.set_parameter( "duration", "500 ms" )
mf_burst.set_parameter( "synapse", MF20Syn_type.id )
mf_burst.set_parameter( "spikeTarget", './{}'.format(MF20Syn_type.id) )
lems_inst_doc.add( mf_burst )
# Add few burst inputs
n_bursts = 4
gocPerm = np.random.permutation( N_goc )
ctr = 0
for gg in range(4):
goc = gocPerm[gg]
for jj in range( n_bursts ):
inst = nml.ExplicitInput( id=ctr, target='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), input=mf_burst.id, synapse=MF20Syn_type.id, spikeTarget='./{}'.format(MF20Syn_type.id))
net.explicit_inputs.append( inst )
ctr += 1
'''
one-to-one pairing of MF and GoC -> no shared inputs
for goc in range(N_mf):
#inst = nml.Instance(id=goc)
#MF_pop.instances.append( inst )
#inst.location = nml.Location( x=GoC_pos[goc,0], y=GoC_pos[goc,1], z=GoC_pos[goc,2]+100 )
#conn = nml.Connection(id=goc, pre_cell_id='../{}/{}/{}'.format(MF_pop.id, goc, mf_inputs.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
#MFprojection.connections.append(conn)
goc2 = N_goc-goc-1
inst2 = nml.Instance(id=goc)
MF_Poisson_pop.instances.append( inst2 )
inst2.location = nml.Location( x=GoC_pos[goc2,0], y=GoC_pos[goc2,1], z=GoC_pos[goc2,2]+100 )
conn2 = nml.Connection(id=goc, pre_cell_id='../{}/{}/{}'.format(MF_Poisson_pop.id, goc, mf_poisson.id), post_cell_id='../{}/{}/{}'.format(goc_pop.id, goc2, goc_type.id), post_segment_id='0', post_fraction_along="0.5")
MF2projection.connections.append(conn2)
'''
# Add electrical synapses
GoCCoupling = nml.ElectricalProjection( id="gocGJ", presynaptic_population=goc_pop.id, postsynaptic_population=goc_pop.id )
#print(GJ_pairs)
gj = nml.GapJunction( id="GJ_0", conductance="426pS" )
net_doc.gap_junctions.append(gj)
nGJ = GJ_pairs.shape[0]
for jj in range( nGJ ):
#gj.append( lems.Component( "GJ_%d"%jj, 'gapJunction') )
#gj[jj].set_parameter( "conductance", "%fnS"%(GJWt[jj]) )
#gj = nml.GapJunction(id="GJ_%d"%jj, conductance="%fnS"%(GJWt[jj]))
#net_doc.gap_junctions.append(gj)
#lems_inst_doc.add( gj[jj] )
#print("%fnS"%(GJWt[jj]*0.426))
conn = nml.ElectricalConnectionInstanceW( id=jj, pre_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj,0], goc_type.id), pre_segment='1', pre_fraction_along='0.5', post_cell='../{}/{}/{}'.format(goc_pop.id, GJ_pairs[jj,1], goc_type.id), post_segment='1', post_fraction_along='0.5', synapse=gj.id, weight=GJWt[jj] )#synapse="GapJuncCML" synapse=gj.id , conductance="100E-9mS"
# ------------ need to create GJ component
GoCCoupling.electrical_connection_instance_ws.append( conn )
net.electrical_projections.append( GoCCoupling )
net_filename = 'gocNetwork.nml'
pynml.write_neuroml2_file( net_doc, net_filename )
lems_filename = 'instances.xml'
pynml.write_lems_file( lems_inst_doc, lems_filename, validate=False )
simid = 'sim_gocnet'+goc_type.id
ls = LEMSSimulation( simid, duration=duration, dt=dt, simulation_seed=seed )
ls.assign_simulation_target( net.id )
#ls.include_lems_file( 'Synapses.xml', include_included=False)
#ls.include_lems_file( 'Inputs.xml', include_included=False)
ls.include_neuroml2_file( net_filename)
ls.include_neuroml2_file( goc_filename)
ls.include_neuroml2_file( GJ_filename)
ls.include_neuroml2_file( MFSyn_filename)
ls.include_neuroml2_file( MF20Syn_filename)
ls.include_lems_file( lems_filename, include_included=False)
# Specify outputs
eof0 = 'Events_file'
ls.create_event_output_file(eof0, "%s.v.spikes"%simid,format='ID_TIME')
for jj in range( goc_pop.size):
ls.add_selection_to_event_output_file( eof0, jj, '{}/{}/{}'.format( goc_pop.id, jj, goc_type.id), 'spike' )
of0 = 'Volts_file'
ls.create_output_file(of0, "%s.v.dat"%simid)
for jj in range( goc_pop.size ):
ls.add_column_to_output_file(of0, jj, '{}/{}/{}/v'.format( goc_pop.id, jj, goc_type.id))
#Create Lems file to run
lems_simfile = ls.save_to_file()
#res = pynml.run_lems_with_jneuroml( lems_simfile, max_memory="1G",nogui=True, plot=False)
#res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", only_generate_scripts = True, compile_mods = False, nogui=True, plot=False)
res = pynml.run_lems_with_jneuroml_neuron( lems_simfile, max_memory="2G", compile_mods = False,nogui=True, plot=False)
#res=True
return res
def generate_grc_layer_network(
runID,
correlationRadius,
NADT,
duration,
dt,
minimumISI, # ms
ONRate, # Hz
OFFRate, # Hz
run=False):
########################################
# Load parameters for this run
file = open('../params_file.pkl', 'r')
p = pkl.load(file)
N_syn = p['N_syn'][int(runID) - 1]
f_mf = p['f_mf'][int(runID) - 1]
run_num = p['run_num'][int(runID) - 1]
file.close()
#################################################################################
# Get connectivity matrix between cells
file = open('../../network_structures/GCLconnectivity_' + str(N_syn) +
'.pkl')
p = pkl.load(file)
conn_mat = p['conn_mat']
N_mf, N_grc = conn_mat.shape
assert (np.all(conn_mat.sum(
axis=0) == N_syn)), 'Connectivity matrix is incorrect.'
# Get MF activity pattern
if correlationRadius == 0: # Activate MFs randomly
N_mf_ON = int(N_mf * f_mf)
mf_indices_ON = random.sample(range(N_mf), N_mf_ON)
mf_indices_ON.sort()
elif correlationRadius > 0: # Spatially correlated MFs
f_mf_range = np.linspace(.05, .95, 19)
f_mf_ix = np.where(f_mf_range == f_mf)[0][0]
p = io.loadmat('../../input_statistics/mf_patterns_r' +
str(correlationRadius) + '.mat')
R = p['Rs'][:, :, f_mf_ix]
g = p['gs'][f_mf_ix]
t = np.dot(R.transpose(), np.random.randn(N_mf))
S = (t > -g * np.ones(N_mf))
mf_indices_ON = np.where(S)[0]
N_mf_ON = len(mf_indices_ON)
#
N_mf_OFF = N_mf - N_mf_ON
mf_indices_OFF = [x for x in range(N_mf) if x not in mf_indices_ON]
mf_indices_OFF.sort()
#################################################################################
# load NeuroML components, LEMS components and LEMS componentTypes from external files
# Spike generator (for Poisson MF spiking)
spike_generator_file_name = "../../grc_lemsDefinitions/spikeGenerators.xml"
spike_generator_doc = pynml.read_lems_file(spike_generator_file_name)
# Integrate-and-fire GC model
# if NADT = 1, loads model GC
iaf_nml2_file_name = "../../grc_lemsDefinitions/IaF_GrC.nml" if NADT == 0 else "../../grc_lemsDefinitions/IaF_GrC_" + '{:.2f}'.format(
f_mf) + ".nml"
iaF_GrC_doc = pynml.read_neuroml2_file(iaf_nml2_file_name)
iaF_GrC = iaF_GrC_doc.iaf_ref_cells[0]
# AMPAR and NMDAR mediated synapses
ampa_syn_filename = "../../grc_lemsDefinitions/RothmanMFToGrCAMPA_" + str(
N_syn) + ".xml"
nmda_syn_filename = "../../grc_lemsDefinitions/RothmanMFToGrCNMDA_" + str(
N_syn) + ".xml"
rothmanMFToGrCAMPA_doc = pynml.read_lems_file(ampa_syn_filename)
rothmanMFToGrCNMDA_doc = pynml.read_lems_file(nmda_syn_filename)
#
# Define components from the componentTypes we just loaded
# Refractory poisson input -- representing active MF
spike_generator_ref_poisson_type = spike_generator_doc.component_types[
'spikeGeneratorRefPoisson']
lems_instances_doc = lems.Model()
spike_generator_on = lems.Component("mossySpikerON",
spike_generator_ref_poisson_type.name)
spike_generator_on.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_on.set_parameter("averageRate", "%s Hz" % ONRate)
lems_instances_doc.add(spike_generator_on)
# Refractory poisson input -- representing silent MF
spike_generator_off = lems.Component("mossySpikerOFF",
spike_generator_ref_poisson_type.name)
spike_generator_off.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_off.set_parameter("averageRate", "%s Hz" % OFFRate)
lems_instances_doc.add(spike_generator_off)
# Synapses
rothmanMFToGrCAMPA = rothmanMFToGrCAMPA_doc.components[
'RothmanMFToGrCAMPA'].id
rothmanMFToGrCNMDA = rothmanMFToGrCNMDA_doc.components[
'RothmanMFToGrCNMDA'].id
#
# Create ON MF, OFF MF, and GC populations
GrCPop = nml.Population(id="GrCPop", component=iaF_GrC.id, size=N_grc)
mossySpikersPopON = nml.Population(id=spike_generator_on.id + "Pop",
component=spike_generator_on.id,
size=N_mf_ON)
mossySpikersPopOFF = nml.Population(id=spike_generator_off.id + "Pop",
component=spike_generator_off.id,
size=N_mf_OFF)
#
# Create network and add populations
net = nml.Network(id="network")
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net.populations.append(GrCPop)
net.populations.append(mossySpikersPopON)
net.populations.append(mossySpikersPopOFF)
#
# MF-GC connectivity
# First connect ON MFs to GCs
for mf_ix_ON in range(N_mf_ON):
mf_ix = mf_indices_ON[mf_ix_ON]
# Find which GCs are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
# Add AMPAR and NMDAR mediated synapses
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.SynapticConnection(
from_='{}[{}]'.format(mossySpikersPopON.id, mf_ix_ON),
synapse=synapse,
to='GrCPop[{}]'.format(grc_ix))
net.synaptic_connections.append(connection)
#
# Now connect OFF MFs to GCs
for mf_ix_OFF in range(N_mf_OFF):
mf_ix = mf_indices_OFF[mf_ix_OFF]
# Find which GCs are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
# Add AMPAR and NMDAR mediated synapses
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.SynapticConnection(
from_='{}[{}]'.format(mossySpikersPopOFF.id, mf_ix_OFF),
synapse=synapse,
to='GrCPop[{}]'.format(grc_ix))
net.synaptic_connections.append(connection)
#
# Write network to file
net_file_name = 'generated_network_' + runID + '.net.nml'
pynml.write_neuroml2_file(net_doc, net_file_name)
# Write LEMS instances to file
lems_instances_file_name = 'instances_' + runID + '.xml'
pynml.write_lems_file(lems_instances_doc,
lems_instances_file_name,
validate=False)
# Create a LEMSSimulation to manage creation of LEMS file
ls = LEMSSimulation('sim_' + runID,
duration,
dt,
lems_seed=int(np.round(1000 * random.random())))
# Point to network as target of simulation
ls.assign_simulation_target(net.id)
# Include generated/existing NeuroML2 files
ls.include_neuroml2_file(iaf_nml2_file_name)
ls.include_lems_file(spike_generator_file_name, include_included=False)
ls.include_lems_file(lems_instances_file_name)
ls.include_lems_file(ampa_syn_filename, include_included=False)
ls.include_lems_file(nmda_syn_filename, include_included=False)
ls.include_neuroml2_file(net_file_name)
# Specify Displays and Output Files
# Details for saving output files
basedir = '../data_r' + str(
correlationRadius) + '/' if NADT == 0 else '../data_r' + str(
correlationRadius) + '_NADT/'
end_filename = str(N_syn) + '_{:.2f}'.format(f_mf) + '_' + str(
run_num) # Add parameter values to spike time filename
# Save MF spike times under basedir + MF_spikes_ + end_filename
eof0 = 'MFspikes_file'
ls.create_event_output_file(eof0,
basedir + "MF_spikes_" + end_filename + ".dat")
# ON MFs
for i in range(mossySpikersPopON.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_ON[i], "%s[%i]" % (mossySpikersPopON.id, i),
'spike')
# OFF MFs
for i in range(mossySpikersPopOFF.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_OFF[i], "%s[%i]" % (mossySpikersPopOFF.id, i),
'spike')
# Save GC spike times under basedir + GrC_spikes_ + end_filename
eof1 = 'GrCspikes_file'
ls.create_event_output_file(
eof1, basedir + "GrC_spikes_" + end_filename + ".dat")
#
for i in range(GrCPop.size):
ls.add_selection_to_event_output_file(eof1, i,
"%s[%i]" % (GrCPop.id, i),
'spike')
#
lems_file_name = ls.save_to_file()
#
if run:
results = pynml.run_lems_with_jneuroml(lems_file_name,
max_memory="8G",
nogui=True,
load_saved_data=False,
plot=False)
return results
def generate_grc_layer_network(
p_mf_ON,
duration,
dt,
minimumISI, # ms
ONRate, # Hz
OFFRate, # Hz
run=False):
# Load connectivity matrix
file = open('GCLconnectivity.pkl')
p = pkl.load(file)
conn_mat = p['conn_mat']
N_mf, N_grc = conn_mat.shape
assert (np.all(conn_mat.sum(
axis=0) == 4)), 'Connectivity matrix is incorrect.'
# Load GrC and MF rosette positions
grc_pos = p['grc_pos']
glom_pos = p['glom_pos']
# Choose which mossy fibers are on, which are off
N_mf_ON = int(N_mf * p_mf_ON)
mf_indices_ON = random.sample(range(N_mf), N_mf_ON)
mf_indices_ON.sort()
N_mf_OFF = N_mf - N_mf_ON
mf_indices_OFF = [x for x in range(N_mf) if x not in mf_indices_ON]
mf_indices_OFF.sort()
# load NeuroML components, LEMS components and LEMS componentTypes from external files
##spikeGeneratorRefPoisson is now a standard nml type...
##spike_generator_doc = pynml.read_lems_file(spike_generator_file_name)
iaF_GrC = nml.IafRefCell(id="iaF_GrC",
refract="2ms",
C="3.22pF",
thresh="-40mV",
reset="-63mV",
leak_conductance="1.498nS",
leak_reversal="-79.67mV")
ampa_syn_filename = "RothmanMFToGrCAMPA.xml"
nmda_syn_filename = "RothmanMFToGrCNMDA.xml"
rothmanMFToGrCAMPA_doc = pynml.read_lems_file(ampa_syn_filename)
rothmanMFToGrCNMDA_doc = pynml.read_lems_file(nmda_syn_filename)
# define some components from the componentTypes we just loaded
##spike_generator_ref_poisson_type = spike_generator_doc.component_types['spikeGeneratorRefPoisson']
lems_instances_doc = lems.Model()
spike_generator_ref_poisson_type_name = 'spikeGeneratorRefPoisson'
spike_generator_on = lems.Component("mossySpikerON",
spike_generator_ref_poisson_type_name)
spike_generator_on.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_on.set_parameter("averageRate", "%s Hz" % ONRate)
lems_instances_doc.add(spike_generator_on)
spike_generator_off = lems.Component(
"mossySpikerOFF", spike_generator_ref_poisson_type_name)
spike_generator_off.set_parameter("minimumISI", "%s ms" % minimumISI)
spike_generator_off.set_parameter("averageRate", "%s Hz" % OFFRate)
lems_instances_doc.add(spike_generator_off)
rothmanMFToGrCAMPA = rothmanMFToGrCAMPA_doc.components[
'RothmanMFToGrCAMPA'].id
rothmanMFToGrCNMDA = rothmanMFToGrCNMDA_doc.components[
'RothmanMFToGrCNMDA'].id
# create populations
GrCPop = nml.Population(id=iaF_GrC.id + "Pop",
component=iaF_GrC.id,
type="populationList",
size=N_grc)
GrCPop.properties.append(nml.Property(tag='color', value='0 0 0.8'))
GrCPop.properties.append(nml.Property(tag='radius', value=2))
mossySpikersPopON = nml.Population(id=spike_generator_on.id + "Pop",
component=spike_generator_on.id,
type="populationList",
size=N_mf_ON)
mossySpikersPopON.properties.append(
nml.Property(tag='color', value='0.8 0 0'))
mossySpikersPopON.properties.append(nml.Property(tag='radius', value=2))
mossySpikersPopOFF = nml.Population(id=spike_generator_off.id + "Pop",
component=spike_generator_off.id,
size=N_mf_OFF)
mossySpikersPopOFF.properties.append(
nml.Property(tag='color', value='0 0.8 0'))
mossySpikersPopOFF.properties.append(nml.Property(tag='radius', value=2))
# create network and add populations
net = nml.Network(id="network")
net_doc = nml.NeuroMLDocument(id=net.id)
net_doc.networks.append(net)
net_doc.iaf_ref_cells.append(iaF_GrC)
net.populations.append(GrCPop)
net.populations.append(mossySpikersPopON)
net.populations.append(mossySpikersPopOFF)
#net_doc.includes.append(nml.IncludeType(href=iaf_nml2_file_name))
# Add locations for GCs
for grc in range(N_grc):
inst = nml.Instance(id=grc)
GrCPop.instances.append(inst)
inst.location = nml.Location(x=grc_pos[grc, 0],
y=grc_pos[grc, 1],
z=grc_pos[grc, 2])
# ON MFs: locations and connectivity
ONprojectionAMPA = nml.Projection(
id="ONProjAMPA",
presynaptic_population=mossySpikersPopON.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCAMPA)
ONprojectionNMDA = nml.Projection(
id="ONProjNMDA",
presynaptic_population=mossySpikersPopON.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCNMDA)
net.projections.append(ONprojectionAMPA)
net.projections.append(ONprojectionNMDA)
ix = 0
for mf_ix_ON in range(N_mf_ON):
mf_ix = mf_indices_ON[mf_ix_ON]
inst = nml.Instance(id=mf_ix_ON)
mossySpikersPopON.instances.append(inst)
inst.location = nml.Location(x=glom_pos[mf_ix, 0],
y=glom_pos[mf_ix, 1],
z=glom_pos[mf_ix, 2])
# find which granule cells are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.Connection(
id=ix,
pre_cell_id='../{}/{}/{}'.format(mossySpikersPopON.id,
mf_ix_ON,
spike_generator_on.id),
post_cell_id='../{}/{}/{}'.format(GrCPop.id, grc_ix,
iaF_GrC.id))
ONprojectionAMPA.connections.append(connection)
ONprojectionNMDA.connections.append(connection)
ix = ix + 1
# OFF MFs: locations and connectivity
OFFprojectionAMPA = nml.Projection(
id="OFFProjAMPA",
presynaptic_population=mossySpikersPopOFF.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCAMPA)
OFFprojectionNMDA = nml.Projection(
id="OFFProjNMDA",
presynaptic_population=mossySpikersPopOFF.id,
postsynaptic_population=GrCPop.id,
synapse=rothmanMFToGrCNMDA)
net.projections.append(OFFprojectionAMPA)
net.projections.append(OFFprojectionNMDA)
ix = 0
for mf_ix_OFF in range(N_mf_OFF):
mf_ix = mf_indices_OFF[mf_ix_OFF]
inst = nml.Instance(id=mf_ix_OFF)
mossySpikersPopOFF.instances.append(inst)
inst.location = nml.Location(x=glom_pos[mf_ix, 0],
y=glom_pos[mf_ix, 1],
z=glom_pos[mf_ix, 2])
# find which granule cells are neighbors
innervated_grcs = np.where(conn_mat[mf_ix, :] == 1)[0]
for grc_ix in innervated_grcs:
for synapse in [rothmanMFToGrCAMPA, rothmanMFToGrCNMDA]:
connection = nml.Connection(
id=ix,
pre_cell_id='../{}/{}/{}'.format(mossySpikersPopOFF.id,
mf_ix_OFF,
spike_generator_on.id),
post_cell_id='../{}/{}/{}'.format(GrCPop.id, grc_ix,
iaF_GrC.id))
OFFprojectionAMPA.connections.append(connection)
OFFprojectionNMDA.connections.append(connection)
ix = ix + 1
# Write network to file
net_file_name = 'OSBnet.nml'
pynml.write_neuroml2_file(net_doc, net_file_name)
# Write LEMS instances to file
lems_instances_file_name = 'instances.xml'
pynml.write_lems_file(lems_instances_doc,
lems_instances_file_name,
validate=False)
# Create a LEMSSimulation to manage creation of LEMS file
ls = LEMSSimulation(
'sim', duration, dt,
simulation_seed=123) # int(np.round(1000*random.random())))
# Point to network as target of simulation
ls.assign_simulation_target(net.id)
# Include generated/existing NeuroML2 files
###ls.include_lems_file(spike_generator_file_name, include_included=False)
ls.include_lems_file(lems_instances_file_name)
ls.include_lems_file(ampa_syn_filename, include_included=False)
ls.include_lems_file(nmda_syn_filename, include_included=False)
ls.include_neuroml2_file(net_file_name)
# Specify Displays and Output Files
basedir = ''
eof0 = 'Volts_file'
ls.create_event_output_file(eof0, basedir + "MF_spikes.dat")
for i in range(mossySpikersPopON.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_ON[i], '{}/{}/{}'.format(mossySpikersPopON.id, i,
spike_generator_on.id),
'spike')
for i in range(mossySpikersPopOFF.size):
ls.add_selection_to_event_output_file(
eof0, mf_indices_OFF[i],
'{}/{}/{}'.format(mossySpikersPopOFF.id, i,
spike_generator_on.id), 'spike')
eof1 = 'GrCspike_file'
ls.create_event_output_file(eof1, basedir + "GrC_spikes.dat")
for i in range(GrCPop.size):
ls.add_selection_to_event_output_file(
eof1, i, '{}/{}/{}'.format(GrCPop.id, i, iaF_GrC.id), 'spike')
lems_file_name = ls.save_to_file()
if run:
print('Running the generated LEMS file: %s for simulation of %sms' %
(lems_file_name, duration))
results = pynml.run_lems_with_jneuroml(lems_file_name,
max_memory="8G",
nogui=True,
load_saved_data=False,
plot=False)
return results
def mdf_to_neuroml(graph, save_to=None, format=None, run_duration_sec=2):
print("Converting graph: %s to NeuroML" % (graph.id))
net = neuromllite.Network(id=graph.id)
net.notes = "NeuroMLlite export of {} graph: {}".format(
format if format else "MDF",
graph.id,
)
model = lems.Model()
lems_definitions = "%s_lems_definitions.xml" % graph.id
for node in graph.nodes:
print(" Node: %s" % node.id)
node_comp_type = "%s__definition" % node.id
node_comp = "%s__instance" % node.id
# Create the ComponentType which defines behaviour of the general class
ct = lems.ComponentType(node_comp_type, extends="baseCellMembPotDL")
ct.add(lems.Attachments("only_input_port", "basePointCurrentDL"))
ct.dynamics.add(
lems.DerivedVariable(name="V",
dimension="none",
value="0",
exposure="V"))
model.add(ct)
# Define the Component - an instance of the ComponentType
comp = lems.Component(node_comp, node_comp_type)
model.add(comp)
cell = neuromllite.Cell(id=node_comp,
lems_source_file=lems_definitions)
net.cells.append(cell)
pop = neuromllite.Population(
id=node.id,
size=1,
component=cell.id,
properties={
"color": "0.2 0.2 0.2",
"radius": 3
},
)
net.populations.append(pop)
if len(node.input_ports) > 1:
raise Exception(
"Currently only max 1 input port supported in NeuroML...")
for ip in node.input_ports:
ct.add(lems.Exposure(ip.id, "none"))
ct.dynamics.add(
lems.DerivedVariable(
name=ip.id,
dimension="none",
select="only_input_port[*]/I",
reduce="add",
exposure=ip.id,
))
on_start = None
for p in node.parameters:
print("Converting %s" % p)
if p.value is not None:
try:
v_num = float(p.value)
ct.add(lems.Parameter(p.id, "none"))
comp.parameters[p.id] = v_num
print(comp.parameters[p.id])
except Exception as e:
ct.add(lems.Exposure(p.id, "none"))
dv = lems.DerivedVariable(
name=p.id,
dimension="none",
value="%s" % (p.value),
exposure=p.id,
)
ct.dynamics.add(dv)
elif p.function is not None:
ct.add(lems.Exposure(p.id, "none"))
func_info = mdf_functions[p.function]
expr = func_info["expression_string"]
expr2 = substitute_args(expr, p.args)
for arg in p.args:
expr += ";{}={}".format(arg, p.args[arg])
dv = lems.DerivedVariable(name=p.id,
dimension="none",
value="%s" % (expr2),
exposure=p.id)
ct.dynamics.add(dv)
else:
ct.add(lems.Exposure(p.id, "none"))
ct.dynamics.add(
lems.StateVariable(name=p.id,
dimension="none",
exposure=p.id))
if p.default_initial_value:
if on_start is None:
on_start = lems.OnStart()
ct.dynamics.add(on_start)
sa = lems.StateAssignment(
variable=p.id,
value=str(evaluate_expr(p.default_initial_value)))
on_start.actions.append(sa)
if p.time_derivative:
td = lems.TimeDerivative(variable=p.id,
value=p.time_derivative)
ct.dynamics.add(td)
if len(node.output_ports) > 1:
raise Exception(
"Currently only max 1 output port supported in NeuroML...")
for op in node.output_ports:
ct.add(lems.Exposure(op.id, "none"))
ct.dynamics.add(
lems.DerivedVariable(name=op.id,
dimension="none",
value=op.value,
exposure=op.id))
only_output_port = "only_output_port"
ct.add(lems.Exposure(only_output_port, "none"))
ct.dynamics.add(
lems.DerivedVariable(
name=only_output_port,
dimension="none",
value=op.id,
exposure=only_output_port,
))
if len(graph.edges) > 0:
model.add(
lems.Include(
os.path.join(os.path.dirname(__file__),
"syn_definitions.xml")))
rsDL = neuromllite.Synapse(id="rsDL",
lems_source_file=lems_definitions)
net.synapses.append(rsDL)
# syn_id = 'silentSyn'
# silentSynDL = neuromllite.Synapse(id=syn_id, lems_source_file=lems_definitions)
for edge in graph.edges:
print(f" Edge: {edge.id} connects {edge.sender} to {edge.receiver}")
ssyn_id = "silentSyn_proj_%s" % edge.id
ssyn_id = "silentSyn_proj_%s" % edge.id
# ssyn_id = 'silentSynX'
silentDLin = neuromllite.Synapse(id=ssyn_id,
lems_source_file=lems_definitions)
model.add(lems.Component(ssyn_id, "silentRateSynapseDL"))
net.synapses.append(silentDLin)
net.projections.append(
neuromllite.Projection(
id="proj_%s" % edge.id,
presynaptic=edge.sender,
postsynaptic=edge.receiver,
synapse=rsDL.id,
pre_synapse=silentDLin.id,
type="continuousProjection",
weight=1,
random_connectivity=neuromllite.RandomConnectivity(
probability=1),
))
# Much more todo...
model.export_to_file(lems_definitions)
print("Nml net: %s" % net)
if save_to:
new_file = net.to_json_file(save_to)
print("Saved NML to: %s" % save_to)
################################################################################
### Build Simulation object & save as JSON
simtime = 1000 * run_duration_sec
dt = 0.1
sim = neuromllite.Simulation(
id="Sim%s" % net.id,
network=new_file,
duration=simtime,
dt=dt,
seed=123,
recordVariables={"OUTPUT": {
"all": "*"
}},
)
recordVariables = {}
for node in graph.nodes:
for ip in node.input_ports:
if not ip.id in recordVariables:
recordVariables[ip.id] = {}
recordVariables[ip.id][node.id] = 0
for p in node.parameters:
if p.is_stateful():
if not p.id in recordVariables:
recordVariables[p.id] = {}
recordVariables[p.id][node.id] = 0
for op in node.output_ports:
if not op.id in recordVariables:
recordVariables[op.id] = {}
recordVariables[op.id][node.id] = 0
sim.recordVariables = recordVariables
if save_to:
sf = sim.to_json_file()
print("Saved Simulation to: %s" % sf)
return net, sim
def handle_population(self,
population_id,
component,
size=-1,
component_obj=None,
properties={}):
sizeInfo = " as yet unspecified size"
if size >= 0:
sizeInfo = ", size: " + str(size) + " cells"
if component_obj:
compInfo = " (%s)" % component_obj.__class__.__name__
else:
compInfo = ""
print_v("Population: " + population_id + ", component: " + component +
compInfo + sizeInfo)
if size >= 0:
for i in range(size):
node_id = "%s_%i" % (population_id, i)
node = {}
node["type"] = {}
node["name"] = node_id
# node['type']['NeuroML'] = component
comp = self.nl_network.get_child(component, "cells")
base_dir = "./" # for now...
fname = locate_file(comp.lems_source_file, base_dir)
model = lems.Model()
model.import_from_file(fname)
lems_comp = model.components.get(component)
print("Cell: [%s] comes from %s and in Lems is: %s" %
(comp, fname, lems_comp))
comp_type = lems_comp.type
type = "The type of %s" % comp_type
function = "The function of %s" % comp_type
if comp_type == "pnlLinearFunctionTM":
function = "Linear"
type = "TransferMechanism"
elif comp_type == "inputNode":
function = "Linear"
type = "TransferMechanism"
elif comp_type == "pnlLogisticFunctionTM":
function = "Logistic"
type = "TransferMechanism"
elif comp_type == "pnlExponentialFunctionTM":
function = "Exponential"
type = "TransferMechanism"
elif comp_type == "pnlSimpleIntegratorMechanism":
function = "SimpleIntegrator"
type = "IntegratorMechanism"
elif comp_type == "fnCell":
function = "FitzHughNagumoIntegrator"
type = "IntegratorMechanism"
node["type"]["PNL"] = type
node["type"]["generic"] = None
node["parameters"] = {}
node["parameters"]["PNL"] = {}
node["functions"] = []
func_info = {}
func_info["type"] = {}
func_info["type"]["generic"] = function
func_info["name"] = "Function_%s" % function
func_info["args"] = {}
for p in lems_comp.parameters:
func_info["args"][p] = {}
func_info["args"][p]["type"] = "float"
func_info["args"][p]["source"] = "%s.input_ports.%s" % (
node_id, p)
if comp.parameters is not None and p in comp.parameters:
func_info["args"][p]["value"] = evaluate(
comp.parameters[p], self.nl_network.parameters)
else:
func_info["args"][p]["value"] = evaluate(
lems_comp.parameters[p]
) # evaluate to ensure strings -> ints/floats etc
node["functions"].append(func_info)
self.bids_mdf_graph["nodes"][node_id] = node
pop_node_id = "%s" % (population_id)
pop_node = {}
pop_node["type"] = {}
pop_node["name"] = pop_node_id
pop_node["type"]["NeuroML"] = component
pop_node["parameters"] = {}
pop_node["parameters"]["size"] = size
pop_node["functions"] = {}
self.bids_mdf_graph_hl["nodes"][pop_node_id] = pop_node
