def runColumnSimulation(simConfig="TempSimConfig",
simDuration=100,
simDt=0.025,
neuroConstructSeed=1234,
simulatorSeed=1234,
simulators=["NEURON"],
simRefPrefix="Net_",
suggestedRemoteRunTime=120,
defaultSynapticDelay=0.05,
mpiConf=MpiSettings.LOCAL_SERIAL,
scaleCortex=0.1,
scaleThalamus=0,
gabaScaling=1,
l4ssAmpaScaling=1,
l5PyrGapScaling=1,
inNrtTcrNmdaScaling=1,
pyrSsNmdaScaling=1,
deepBiasCurrent=-1,
maxElecLenFRB=0.01,
maxElecLenRS=0.01,
maxElecLenIN=0.01,
maxElecLenSS=0.01,
somaNseg=-1,
varTimestepNeuron=False,
verbose=True,
runInBackground=False):
print "Running new column simulation..."
############################################
# Full column populations
numFRB = 50 # full model: 50
numRS = 1000 # full model: 1000
numSupBask = 90 # full model: 90
numSupAxAx = 90 # full model: 90
numSupLTS = 90 # full model: 90
numL4SpinStell = 240 # full model: 240
numL5TuftIB = 800 # full model: 800
numL5TuftRS = 200 # full model: 200
numDeepBask = 100 # full model: 100
numDeepAxAx = 100 # full model: 100
numDeepLTS = 100 # full model: 100
numL6NonTuftRS = 500 # full model: 500
numTCR = 100 # full model: 100
numnRT = 100 # full model: 100
#######################################
### Load neuroConstruct project
import datetime
start = datetime.datetime.now()
print "Loading project %s from at %s " % (projFile.getCanonicalPath(),
start.strftime("%Y-%m-%d %H:%M"))
pm = ProjectManager()
project = pm.loadProject(projFile)
### Set duration & timestep & simulation configuration
project.simulationParameters.setDt(simDt)
simConfig = project.simConfigInfo.getSimConfig(simConfig)
simConfig.setSimDuration(simDuration)
### Set simulation reference
index = 0
simRef = "%s%i" % (simRefPrefix, index)
while File("%s/simulations/%s_N" %
(project.getProjectMainDirectory().getCanonicalPath(),
simRef)).exists():
simRef = "%s%i" % (simRefPrefix, index)
index = index + 1
project.simulationParameters.setReference(simRef)
### Change num in each cell group
numFRB = int(scaleCortex * numFRB)
numRS = int(scaleCortex * numRS)
numSupBask = int(scaleCortex * numSupBask)
numSupAxAx = int(scaleCortex * numSupAxAx)
numSupLTS = int(scaleCortex * numSupLTS)
numL4SpinStell = int(scaleCortex * numL4SpinStell)
numL5TuftIB = int(scaleCortex * numL5TuftIB)
numL5TuftRS = int(scaleCortex * numL5TuftRS)
numDeepBask = int(scaleCortex * numDeepBask)
numDeepAxAx = int(scaleCortex * numDeepAxAx)
numDeepLTS = int(scaleCortex * numDeepLTS)
numL6NonTuftRS = int(scaleCortex * numL6NonTuftRS)
numTCR = int(scaleThalamus * numTCR)
numnRT = int(scaleThalamus * numnRT)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L23PyrFRB").setMaxNumberCells(
numFRB) # Note only works if RandomCellPackingAdapter
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L23PyrRS").setMaxNumberCells(numRS)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_SupBask").setMaxNumberCells(numSupBask)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_SupAxAx").setMaxNumberCells(numSupAxAx)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_SupLTS").setMaxNumberCells(numSupLTS)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L4SpinStell").setMaxNumberCells(numL4SpinStell)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L5TuftIB").setMaxNumberCells(numL5TuftIB)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L5TuftRS").setMaxNumberCells(numL5TuftRS)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_DeepBask").setMaxNumberCells(numDeepBask)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_DeepAxAx").setMaxNumberCells(numDeepAxAx)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_DeepLTS").setMaxNumberCells(numDeepLTS)
project.cellGroupsInfo.getCellPackingAdapter(
"CG3D_L6NonTuftRS").setMaxNumberCells(numL6NonTuftRS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_TCR").setMaxNumberCells(
numTCR)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_nRT").setMaxNumberCells(
numnRT)
### Change weights in synapses/gap junctions
for netConnName in simConfig.getNetConns():
if gabaScaling != 1:
SimulationsInfo.addExtraSimProperty("gabaWeightScaling",
str(gabaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).getSynapseType()
if synName.count("GABAA") > 0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f" % (
synName, netConnName, gabaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).setWeightsGenerator(
NumberGenerator(gabaScaling))
if l4ssAmpaScaling != 1:
SimulationsInfo.addExtraSimProperty("l4ssAmpaScaling",
str(l4ssAmpaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).getSynapseType()
if synName.count("Syn_AMPA_L4SS_L4SS") > 0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f" % (
synName, netConnName, l4ssAmpaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).setWeightsGenerator(
NumberGenerator(l4ssAmpaScaling))
if l5PyrGapScaling != 1:
SimulationsInfo.addExtraSimProperty("l5PyrGapScaling",
str(l5PyrGapScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).getSynapseType()
tgtCG = project.morphNetworkConnectionsInfo.getTargetCellGroup(
netConnName)
if synName.count("Syn_Elect_DeepPyr_DeepPyr"
) > 0 and tgtCG.count("CG3D_L5") > 0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f" % (
synName, netConnName, l5PyrGapScaling)
project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).setWeightsGenerator(
NumberGenerator(l5PyrGapScaling))
if inNrtTcrNmdaScaling != 1:
SimulationsInfo.addExtraSimProperty("inNrtTcrNmdaScaling",
str(inNrtTcrNmdaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).getSynapseType()
if synName.count("NMDA") > 0 and (
synName.endswith("_IN") > 0
or synName.endswith("_DeepIN") > 0
or synName.endswith("_SupIN") > 0
or synName.endswith("_SupFS") > 0
or synName.endswith("_DeepFS") > 0
or synName.endswith("_SupLTS") > 0
or synName.endswith("_DeepLTS") > 0
or synName.endswith("_nRT") > 0
or synName.endswith("_TCR") > 0):
print "Changing synaptic weight for syn %s in net conn %s by factor %f" % (
synName, netConnName, inNrtTcrNmdaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).setWeightsGenerator(
NumberGenerator(inNrtTcrNmdaScaling))
if pyrSsNmdaScaling != 1:
SimulationsInfo.addExtraSimProperty("pyrSsNmdaScaling",
str(pyrSsNmdaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).getSynapseType()
if synName.count("NMDA") > 0 and (
synName.endswith("_SupPyr") > 0
or synName.endswith("_DeepPyr") > 0
or synName.endswith("_L4SS") > 0
or synName.endswith("_L5Pyr") > 0
or synName.endswith("_L5RS") > 0
or synName.endswith("_L5IB") > 0
or synName.endswith("_L6NT") > 0
or synName.endswith("_Pyr") > 0):
print "Changing synaptic weight for syn %s in net conn %s by factor %f" % (
synName, netConnName, pyrSsNmdaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(
netConnName).get(index).setWeightsGenerator(
NumberGenerator(pyrSsNmdaScaling))
### Change bias currents
for inputName in simConfig.getInputs():
stim = project.elecInputInfo.getStim(inputName)
if deepBiasCurrent >= 0:
if stim.getElectricalInput().TYPE == "IClamp" and (
stim.getCellGroup() == "CG3D_L5TuftIB"
or stim.getCellGroup() == "CG3D_L5TuftRS"
or stim.getCellGroup() == "CG3D_L6NonTuftRS"):
print "Changing offset current in %s to %f" % (
stim.getCellGroup(), deepBiasCurrent)
stim.setAmp(NumberGenerator(deepBiasCurrent))
project.elecInputInfo.updateStim(stim)
### Change spatial discretisation of some cells. This will impact accuracy & simulation speed
if maxElecLenFRB > 0:
frbCell = project.cellManager.getCell("L23PyrFRB_varInit")
info = CellTopologyHelper.recompartmentaliseCell(
frbCell, maxElecLenFRB, project)
print "Recompartmentalised FRB cell: " + info
if somaNseg > 0:
frbCell.getSegmentWithId(
0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenRS > 0:
rsCell = project.cellManager.getCell("L23PyrRS")
info = CellTopologyHelper.recompartmentaliseCell(
rsCell, maxElecLenRS, project)
print "Recompartmentalised RS cell: " + info
if somaNseg > 0:
rsCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(
somaNseg)
if maxElecLenIN > 0:
inCells = [
"SupBasket", "SupAxAx", "SupLTSInter", "DeepBasket", "DeepAxAx",
"DeepLTSInter"
]
for inCellName in inCells:
inCell = project.cellManager.getCell(inCellName)
info = CellTopologyHelper.recompartmentaliseCell(
inCell, maxElecLenIN, project)
print "Recompartmentalised " + inCellName + " cell: " + info
if somaNseg > 0:
inCell.getSegmentWithId(
0).getSection().setNumberInternalDivisions(somaNseg)
### Change parallel configuration
mpiSettings = MpiSettings()
simConfig.setMpiConf(mpiSettings.getMpiConfiguration(mpiConf))
print "Parallel configuration: " + str(simConfig.getMpiConf())
if suggestedRemoteRunTime > 0:
project.neuronFileManager.setSuggestedRemoteRunTime(
suggestedRemoteRunTime)
project.genesisFileManager.setSuggestedRemoteRunTime(
suggestedRemoteRunTime)
### Change synaptic delay associated with each net conn
for netConnName in simConfig.getNetConns():
if netConnName.count("gap") == 0:
print "Changing synaptic delay in %s to %f" % (
netConnName, defaultSynapticDelay)
delayGen = NumberGenerator(defaultSynapticDelay)
for synProps in project.morphNetworkConnectionsInfo.getSynapseList(
netConnName):
synProps.setDelayGenerator(delayGen)
# defaultSynapticDelay will be recorded in simulation.props and listed in SimulationBrowser GUI
SimulationsInfo.addExtraSimProperty("defaultSynapticDelay",
str(defaultSynapticDelay))
### Generate network structure in neuroConstruct
pm.doGenerate(simConfig.getName(), neuroConstructSeed)
while pm.isGenerating():
print "Waiting for the project to be generated with Simulation Configuration: " + str(
simConfig)
sleep(2)
print "Generated %i cells in %i cell groups" % (
project.generatedCellPositions.getNumberInAllCellGroups(),
project.generatedCellPositions.getNumberNonEmptyCellGroups())
print "Generated %i instances in %i network connections" % (
project.generatedNetworkConnections.getNumAllSynConns(),
project.generatedNetworkConnections.getNumNonEmptyNetConns())
print "Generated %i instances in %i elect inputs" % (
project.generatedElecInputs.getNumberSingleInputs(),
project.generatedElecInputs.getNonEmptyInputRefs().size())
if simulators.count("NEURON") > 0:
simRefN = simRef + "_N"
project.simulationParameters.setReference(simRefN)
nc.generateAndRunNeuron(project,
pm,
simConfig,
simRefN,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground,
varTimestep=varTimestepNeuron)
sleep(2) # wait a while before running GENESIS...
if simulators.count("GENESIS") > 0:
simRefG = simRef + "_G"
project.simulationParameters.setReference(simRefG)
nc.generateAndRunGenesis(project,
pm,
simConfig,
simRefG,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground)
sleep(2) # wait a while before running MOOSE...
if simulators.count("MOOSE") > 0:
simRefM = simRef + "_M"
project.simulationParameters.setReference(simRefM)
nc.generateAndRunMoose(project,
pm,
simConfig,
simRefM,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground)
sleep(2) # wait a while before running GENESIS...
print "Finished running all sims, shutting down..."
stop = datetime.datetime.now()
print
print "Started: %s, finished: %s" % (start.strftime("%Y-%m-%d %H:%M"),
stop.strftime("%Y-%m-%d %H:%M"))
print
for chan in reduced_cell_type.getChanMechsForGroup('soma_group'):
if chan.getName() in ['NaP_CML', 'NaR_CML', 'NaT_CML']:
chan.setDensity(0)
reduced_cell_type.associateGroupWithChanMech('soma_group', chan)
for chan in vervaeke_cell_type.getChanMechsForGroup('soma_group'):
if chan.getName() in ['NaP', 'NaR', 'NaT']:
chan.setDensity(0)
vervaeke_cell_type.associateGroupWithChanMech('soma_group', chan)
# generate
pm.doGenerate(sim_config_name, 1234)
while pm.isGenerating():
time.sleep(0.02)
# pick a segment to stimulate on the detailed cell
cth = CellTopologyHelper()
distances_dict = dict(cth.getSegmentDistancesFromRoot(vervaeke_cell_type, dendritic_group))
stim_seg_detailed = random.choice([seg_id for seg_id,dist in distances_dict.items() if 200. < dist < 220.])
# pick a segment to stimulate on the reduced cell
stim_seg_reduced = 6
# map distances of all ancestors of stimulation segment
ancestor_dists = dict(cth.getDistancesFromAncestorSegments(vervaeke_cell_type, stim_seg_detailed))
source_segment_index = 0
source_fraction_along = 0.5
delay = 0.
locs = []
detailed_seg_ids = []
if deepBiasCurrent >= 0:
if stim.getCellGroup()=="CG3D_L5TuftIB" or stim.getCellGroup()=="CG3D_L5TuftRS" or stim.getCellGroup()=="CG3D_L6NonTuftRS":
print "Changing offset current in %s to %f"%(stim.getCellGroup(), deepBiasCurrent)
stim.setAmp(NumberGenerator(deepBiasCurrent))
project.elecInputInfo.updateStim(stim)'''
### Change spatial discretisation of some cells. This will impact accuracy & simulation speed
if maxElecLenFRB > 0:
frbCell = project.cellManager.getCell("L23PyrFRB_varInit")
info = CellTopologyHelper.recompartmentaliseCell(frbCell, maxElecLenFRB, project)
print "Recompartmentalised FRB cell: "+info
if somaNseg > 0:
frbCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenRS > 0:
rsCell = project.cellManager.getCell("L23PyrRS")
info = CellTopologyHelper.recompartmentaliseCell(rsCell, maxElecLenRS, project)
print "Recompartmentalised RS cell: "+info
if somaNseg > 0:
rsCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenIN > 0:
inCells = ["SupBasket", "SupAxAx","SupLTSInter","DeepBasket", "DeepAxAx","DeepLTSInter"]
for inCellName in inCells:
def runColumnSimulation(simConfig= "TempSimConfig",
simDuration = 100,
simDt = 0.025,
neuroConstructSeed = 1234,
simulatorSeed = 1234,
simulators = ["NEURON"],
simRefPrefix = "Net_",
suggestedRemoteRunTime = 120,
defaultSynapticDelay = 0.05,
mpiConf = MpiSettings.LOCAL_SERIAL,
scaleCortex = 0.1,
scaleThalamus = 0,
gabaScaling = 1,
l4ssAmpaScaling = 1,
l5PyrGapScaling = 1,
inNrtTcrNmdaScaling = 1,
pyrSsNmdaScaling = 1,
deepBiasCurrent = -1,
maxElecLenFRB = 0.01,
maxElecLenRS = 0.01,
maxElecLenIN = 0.01,
maxElecLenSS = 0.01,
somaNseg = -1,
varTimestepNeuron = False,
verbose = True,
runInBackground = False):
print "Running new column simulation..."
############################################
# Full column populations
numFRB = 50 # full model: 50
numRS = 1000 # full model: 1000
numSupBask = 90 # full model: 90
numSupAxAx = 90 # full model: 90
numSupLTS = 90 # full model: 90
numL4SpinStell = 240 # full model: 240
numL5TuftIB = 800 # full model: 800
numL5TuftRS = 200 # full model: 200
numDeepBask = 100 # full model: 100
numDeepAxAx = 100 # full model: 100
numDeepLTS = 100 # full model: 100
numL6NonTuftRS = 500 # full model: 500
numTCR = 100 # full model: 100
numnRT = 100 # full model: 100
#######################################
### Load neuroConstruct project
import datetime
start = datetime.datetime.now()
print "Loading project %s from at %s " % (projFile.getCanonicalPath(),start.strftime("%Y-%m-%d %H:%M"))
pm = ProjectManager()
project = pm.loadProject(projFile)
### Set duration & timestep & simulation configuration
project.simulationParameters.setDt(simDt)
simConfig = project.simConfigInfo.getSimConfig(simConfig)
simConfig.setSimDuration(simDuration)
### Set simulation reference
index = 0
simRef = "%s%i"%(simRefPrefix,index)
while File( "%s/simulations/%s_N"%(project.getProjectMainDirectory().getCanonicalPath(), simRef)).exists():
simRef = "%s%i"%(simRefPrefix,index)
index = index+1
project.simulationParameters.setReference(simRef)
### Change num in each cell group
numFRB = int(scaleCortex * numFRB)
numRS = int(scaleCortex * numRS)
numSupBask = int(scaleCortex * numSupBask)
numSupAxAx = int(scaleCortex * numSupAxAx)
numSupLTS = int(scaleCortex * numSupLTS)
numL4SpinStell = int(scaleCortex * numL4SpinStell)
numL5TuftIB = int(scaleCortex * numL5TuftIB)
numL5TuftRS = int(scaleCortex * numL5TuftRS)
numDeepBask = int(scaleCortex * numDeepBask)
numDeepAxAx = int(scaleCortex * numDeepAxAx)
numDeepLTS = int(scaleCortex * numDeepLTS)
numL6NonTuftRS = int(scaleCortex * numL6NonTuftRS)
numTCR = int(scaleThalamus * numTCR)
numnRT = int(scaleThalamus * numnRT)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L23PyrFRB").setMaxNumberCells(numFRB) # Note only works if RandomCellPackingAdapter
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L23PyrRS").setMaxNumberCells(numRS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_SupBask").setMaxNumberCells(numSupBask)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_SupAxAx").setMaxNumberCells(numSupAxAx)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_SupLTS").setMaxNumberCells(numSupLTS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L4SpinStell").setMaxNumberCells(numL4SpinStell)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L5TuftIB").setMaxNumberCells(numL5TuftIB)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L5TuftRS").setMaxNumberCells(numL5TuftRS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_DeepBask").setMaxNumberCells(numDeepBask)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_DeepAxAx").setMaxNumberCells(numDeepAxAx)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_DeepLTS").setMaxNumberCells(numDeepLTS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_L6NonTuftRS").setMaxNumberCells(numL6NonTuftRS)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_TCR").setMaxNumberCells(numTCR)
project.cellGroupsInfo.getCellPackingAdapter("CG3D_nRT").setMaxNumberCells(numnRT)
### Change weights in synapses/gap junctions
for netConnName in simConfig.getNetConns():
if gabaScaling != 1:
SimulationsInfo.addExtraSimProperty("gabaWeightScaling", str(gabaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).getSynapseType()
if synName.count("GABAA")>0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f"%(synName, netConnName, gabaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).setWeightsGenerator(NumberGenerator(gabaScaling))
if l4ssAmpaScaling !=1:
SimulationsInfo.addExtraSimProperty("l4ssAmpaScaling", str(l4ssAmpaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).getSynapseType()
if synName.count("Syn_AMPA_L4SS_L4SS")>0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f"%(synName, netConnName, l4ssAmpaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).setWeightsGenerator(NumberGenerator(l4ssAmpaScaling))
if l5PyrGapScaling !=1:
SimulationsInfo.addExtraSimProperty("l5PyrGapScaling", str(l5PyrGapScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).getSynapseType()
tgtCG = project.morphNetworkConnectionsInfo.getTargetCellGroup(netConnName)
if synName.count("Syn_Elect_DeepPyr_DeepPyr")>0 and tgtCG.count("CG3D_L5")>0:
print "Changing synaptic weight for syn %s in net conn %s by factor %f"%(synName, netConnName, l5PyrGapScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).setWeightsGenerator(NumberGenerator(l5PyrGapScaling))
if inNrtTcrNmdaScaling !=1:
SimulationsInfo.addExtraSimProperty("inNrtTcrNmdaScaling", str(inNrtTcrNmdaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).getSynapseType()
if synName.count("NMDA")>0 and (synName.endswith("_IN")>0 or
synName.endswith("_DeepIN")>0 or
synName.endswith("_SupIN")>0 or
synName.endswith("_SupFS")>0 or
synName.endswith("_DeepFS")>0 or
synName.endswith("_SupLTS")>0 or
synName.endswith("_DeepLTS")>0 or
synName.endswith("_nRT")>0 or
synName.endswith("_TCR")>0):
print "Changing synaptic weight for syn %s in net conn %s by factor %f"%(synName, netConnName, inNrtTcrNmdaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).setWeightsGenerator(NumberGenerator(inNrtTcrNmdaScaling))
if pyrSsNmdaScaling !=1:
SimulationsInfo.addExtraSimProperty("pyrSsNmdaScaling", str(pyrSsNmdaScaling))
synList = project.morphNetworkConnectionsInfo.getSynapseList(netConnName)
for index in range(0, len(synList)):
synName = project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).getSynapseType()
if synName.count("NMDA")>0 and (synName.endswith("_SupPyr")>0 or
synName.endswith("_DeepPyr")>0 or
synName.endswith("_L4SS")>0 or
synName.endswith("_L5Pyr")>0 or
synName.endswith("_L5RS")>0 or
synName.endswith("_L5IB")>0 or
synName.endswith("_L6NT")>0 or
synName.endswith("_Pyr")>0):
print "Changing synaptic weight for syn %s in net conn %s by factor %f"%(synName, netConnName, pyrSsNmdaScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(index).setWeightsGenerator(NumberGenerator(pyrSsNmdaScaling))
### Change bias currents
for inputName in simConfig.getInputs():
stim = project.elecInputInfo.getStim(inputName)
if deepBiasCurrent >= 0:
if stim.getElectricalInput().TYPE == "IClamp" and (stim.getCellGroup()=="CG3D_L5TuftIB" or stim.getCellGroup()=="CG3D_L5TuftRS" or stim.getCellGroup()=="CG3D_L6NonTuftRS"):
print "Changing offset current in %s to %f"%(stim.getCellGroup(), deepBiasCurrent)
stim.setAmp(NumberGenerator(deepBiasCurrent))
project.elecInputInfo.updateStim(stim)
### Change spatial discretisation of some cells. This will impact accuracy & simulation speed
if maxElecLenFRB > 0:
frbCell = project.cellManager.getCell("L23PyrFRB_varInit")
info = CellTopologyHelper.recompartmentaliseCell(frbCell, maxElecLenFRB, project)
print "Recompartmentalised FRB cell: "+info
if somaNseg > 0:
frbCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenRS > 0:
rsCell = project.cellManager.getCell("L23PyrRS")
info = CellTopologyHelper.recompartmentaliseCell(rsCell, maxElecLenRS, project)
print "Recompartmentalised RS cell: "+info
if somaNseg > 0:
rsCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenIN > 0:
inCells = ["SupBasket", "SupAxAx","SupLTSInter","DeepBasket", "DeepAxAx","DeepLTSInter"]
for inCellName in inCells:
inCell = project.cellManager.getCell(inCellName)
info = CellTopologyHelper.recompartmentaliseCell(inCell, maxElecLenIN, project)
print "Recompartmentalised "+inCellName+" cell: "+info
if somaNseg > 0:
inCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
### Change parallel configuration
mpiSettings = MpiSettings()
simConfig.setMpiConf(mpiSettings.getMpiConfiguration(mpiConf))
print "Parallel configuration: "+ str(simConfig.getMpiConf())
if suggestedRemoteRunTime > 0:
project.neuronFileManager.setSuggestedRemoteRunTime(suggestedRemoteRunTime)
project.genesisFileManager.setSuggestedRemoteRunTime(suggestedRemoteRunTime)
### Change synaptic delay associated with each net conn
for netConnName in simConfig.getNetConns():
if netConnName.count("gap")==0:
print "Changing synaptic delay in %s to %f"%(netConnName, defaultSynapticDelay)
delayGen = NumberGenerator(defaultSynapticDelay)
for synProps in project.morphNetworkConnectionsInfo.getSynapseList(netConnName):
synProps.setDelayGenerator(delayGen)
# defaultSynapticDelay will be recorded in simulation.props and listed in SimulationBrowser GUI
SimulationsInfo.addExtraSimProperty("defaultSynapticDelay", str(defaultSynapticDelay))
### Generate network structure in neuroConstruct
pm.doGenerate(simConfig.getName(), neuroConstructSeed)
while pm.isGenerating():
print "Waiting for the project to be generated with Simulation Configuration: "+str(simConfig)
sleep(2)
print "Generated %i cells in %i cell groups" % (project.generatedCellPositions.getNumberInAllCellGroups(), project.generatedCellPositions.getNumberNonEmptyCellGroups())
print "Generated %i instances in %i network connections" % (project.generatedNetworkConnections.getNumAllSynConns(), project.generatedNetworkConnections.getNumNonEmptyNetConns())
print "Generated %i instances in %i elect inputs" % (project.generatedElecInputs.getNumberSingleInputs(), project.generatedElecInputs.getNonEmptyInputRefs().size())
if simulators.count("NEURON")>0:
simRefN = simRef+"_N"
project.simulationParameters.setReference(simRefN)
nc.generateAndRunNeuron(project,
pm,
simConfig,
simRefN,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground,
varTimestep=varTimestepNeuron)
sleep(2) # wait a while before running GENESIS...
if simulators.count("GENESIS")>0:
simRefG = simRef+"_G"
project.simulationParameters.setReference(simRefG)
nc.generateAndRunGenesis(project,
pm,
simConfig,
simRefG,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground)
sleep(2) # wait a while before running MOOSE...
if simulators.count("MOOSE")>0:
simRefM = simRef+"_M"
project.simulationParameters.setReference(simRefM)
nc.generateAndRunMoose(project,
pm,
simConfig,
simRefM,
simulatorSeed,
verbose=verbose,
runInBackground=runInBackground)
sleep(2) # wait a while before running GENESIS...
print "Finished running all sims, shutting down..."
stop = datetime.datetime.now()
print
print "Started: %s, finished: %s" % (start.strftime("%Y-%m-%d %H:%M"),stop.strftime("%Y-%m-%d %H:%M"))
print
def SingleCellNML2generator(projString=" ",
ConfigDict={},
ElecLenList=[],
somaNseg=None,
savingDir=None):
projFile = File(os.getcwd(), projString)
pm = ProjectManager()
compSummary = {}
for config in ConfigDict.keys():
project = pm.loadProject(projFile)
nmlfm = NeuroMLFileManager(project)
compSummary[config] = {}
if " " in config:
configPath = config.replace(" ", "_")
else:
configPath = config
for maxElecLen in ElecLenList:
compSummary[config][str(maxElecLen)] = {}
cell = project.cellManager.getCell(ConfigDict[config])
if maxElecLen > 0:
info = CellTopologyHelper.recompartmentaliseCell(
cell, maxElecLen, project)
print "Recompartmentalising cell %s" % ConfigDict[config]
if somaNseg != None:
cell.getSegmentWithId(
0).getSection().setNumberInternalDivisions(somaNseg)
if savingDir != None:
cellpath = r'../../NeuroML2/%s/%s/%s_%f' % (
savingDir, configPath, configPath, maxElecLen)
else:
cellpath = r'../../NeuroML2/%s/%s_%f' % (
configPath, configPath, maxElecLen)
else:
if savingDir != None:
cellpath = r'../../NeuroML2/%s/%s/%s_default' % (
savingDir, configPath, configPath)
else:
cellpath = r'../../NeuroML2/%s/%s_default' % (configPath,
configPath)
summary = str(cell.getMorphSummary())
summary_string = summary.split("_")
for feature in summary_string:
feature_split = feature.split(":")
compSummary[config][str(maxElecLen)][
feature_split[0]] = feature_split[1]
# the format of summary : Segs:122_Secs:61_IntDivs:1458
print("Will be printing a cell morphology summary")
print compSummary[config][str(maxElecLen)]
######### it turns out that this does not save recompartmentalized cells - all saved cells have identical spatial discretization;
##### generateNeuroML2 receives the parent projFile but not the loaded project which is modified by the CellTopologyHelper.recompartmentaliseCell()
##################### neuroConstruct block #############################################################################
neuroConstructSeed = 1234
verbose = True
pm.doGenerate(config, neuroConstructSeed)
while pm.isGenerating():
if verbose:
print(
"Waiting for the project to be generated with Simulation Configuration: "
+ config)
time.sleep(5)
simConfig = project.simConfigInfo.getSimConfig(config)
seed = 1234
genDir = File(projFile.getParentFile(), "generatedNeuroML2")
nmlfm.generateNeuroMLFiles(
simConfig, NeuroMLConstants.NeuroMLVersion.getLatestVersion(),
LemsConstants.LemsOption.LEMS_WITHOUT_EXECUTE_MODEL,
OriginalCompartmentalisation(), seed, False, True, genDir,
"GENESIS Physiological Units", False)
########################################################################################################################
if not os.path.exists(cellpath):
print("Creating a new directory %s" % cellpath)
os.makedirs(cellpath)
else:
print("A directory %s already exists" % cellpath)
src_files = os.listdir("../generatedNeuroML2/")
for file_name in src_files:
full_file_name = os.path.join("../generatedNeuroML2/",
file_name)
if (os.path.isfile(full_file_name)):
print("Moving generated NeuroML2 to files to %s" %
cellpath)
shutil.copy(full_file_name, cellpath)
#with open("compSummary.json",'w') as fout:
#json.dump(compSummary, fout)
subprocess.call(["~/neuroConstruct/nC.sh -python RegenerateNml2.py -f"],
shell=True)
#subprocess.call(["cp compSummary.json ~/Thalamocortical/NeuroML2/"],shell=True)
quit()
for simConfigName in simConfigs:
for maxElecLen in maxElecLens:
project.simulationParameters.setDt(simDt)
simConfig = project.simConfigInfo.getSimConfig(simConfigName)
recompSuffix = ""
if maxElecLen > 0:
cellGroup = simConfig.getCellGroups().get(0)
cell = project.cellManager.getCell(project.cellGroupsInfo.getCellType(cellGroup))
print "Recomp cell in: "+cellGroup+" which is: "+str(cell)
info = CellTopologyHelper.recompartmentaliseCell(cell, maxElecLen, project)
print "*** Recompartmentalised cell: "+info
recompSuffix = "_"+str(maxElecLen)
pm.doGenerate(simConfig.getName(), neuroConstructSeed)
while pm.isGenerating():
if verbose: print "Waiting for the project to be generated with Simulation Configuration: "+str(simConfig)
time.sleep(1)
print "Generated network with %i cell(s)" % project.generatedCellPositions.getNumberInAllCellGroups()
simRefPrefix = (simConfigName+"_").replace(' ', '')
def SingleCellNML2generator(projString=" ",ConfigDict={},ElecLenList=[],somaNseg=None,savingDir=None,shell=None):
projFile=File(os.getcwd(),projString)
pm=ProjectManager()
for config in ConfigDict.keys():
project=pm.loadProject(projFile)
nmlfm = NeuroMLFileManager(project)
compSummary={}
compSummary[config]={}
if " " in config:
configPath=config.replace(" ","_")
else:
configPath=config
if savingDir !=None:
full_path_to_config=r'../%s/%s'%(savingDir,configPath)
else:
full_path_to_config=r'../%s'%(configPath)
for maxElecLen in ElecLenList:
compSummary[config][str(maxElecLen)]={}
cell=project.cellManager.getCell(ConfigDict[config])
if maxElecLen > 0:
info = CellTopologyHelper.recompartmentaliseCell(cell, maxElecLen, project)
print "Recompartmentalising cell %s"%ConfigDict[config]
if somaNseg != None:
cell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if savingDir !=None:
cellpath = r'../%s/%s/%s_%f'%(savingDir,configPath,configPath,maxElecLen)
else:
cellpath = r'../%s/%s_%f'%(configPath,configPath,maxElecLen)
else:
if savingDir !=None:
cellpath = r'../%s/%s/%s_default'%(savingDir,configPath,configPath)
else:
cellpath = r'../%s/%s_default'%(configPath,configPath)
summary=str(cell.getMorphSummary())
summary_string=summary.split("_")
for feature in summary_string:
feature_split=feature.split(":")
compSummary[config][str(maxElecLen)][feature_split[0]]=feature_split[1]
# the format of summary : Segs:122_Secs:61_IntDivs:1458
print("Will be printing a cell morphology summary")
print compSummary[config][str(maxElecLen)]
######### it turns out that this does not save recompartmentalized cells - all saved cells have identical spatial discretization;
##### generateNeuroML2 receives the parent projFile but not the loaded project which is modified by the CellTopologyHelper.recompartmentaliseCell()
##################### neuroConstruct block #############################################################################
neuroConstructSeed=1234
verbose=True
pm.doGenerate(config, neuroConstructSeed)
while pm.isGenerating():
if verbose:
print("Waiting for the project to be generated with Simulation Configuration: "+config)
time.sleep(5)
simConfig = project.simConfigInfo.getSimConfig(config)
seed=1234
genDir = File(projFile.getParentFile(), "generatedNeuroML2")
nmlfm.generateNeuroMLFiles(simConfig,
NeuroMLConstants.NeuroMLVersion.getLatestVersion(),
LemsConstants.LemsOption.LEMS_WITHOUT_EXECUTE_MODEL,
OriginalCompartmentalisation(),
seed,
False,
True,
genDir,
"GENESIS Physiological Units",
False)
########################################################################################################################
if not os.path.exists(cellpath):
print("Creating a new directory %s"%cellpath)
os.makedirs(cellpath)
else:
print("A directory %s already exists"%cellpath)
src_files = os.listdir("../../neuroConstruct/generatedNeuroML2/")
for file_name in src_files:
full_file_name = os.path.join("../../neuroConstruct/generatedNeuroML2/", file_name)
if (os.path.isfile(full_file_name)):
print("Moving generated NeuroML2 to files to %s"%cellpath)
shutil.copy(full_file_name, cellpath)
with open(os.path.join(full_path_to_config,"compSummary.json"),'w') as fout:
json.dump(compSummary, fout)
if shell ==None:
extension='sh'
else:
extension=shell
os.chdir("../../neuroConstruct/pythonScripts")
subprocess.call("%s/nC.%s -python ../../neuroConstruct/pythonScripts/RegenerateNml2.py -f"%(os.environ["NC_HOME"],extension),shell=True)
os.chdir("../../NeuroML2/pythonScripts")
sp1 = Species("k")
sp1.setProperty("e", "-77.7")
grps1 = Vector()
grps1.add(Section.ALL)
spVg.put(sp1, grps1)
sp2 = Species("na")
sp2.setProperty("e", "55.5")
grps2 = Vector()
grps2.add(Section.ALL)
spVg.put(sp2, grps2)
print "Spec vs groups: " + str(spVg)
print CellTopologyHelper.printDetails(cell, myProject)
simConfig = myProject.simConfigInfo.getSimConfig(simConfig)
myProject.markProjectAsEdited()
print "Saving project..."
myProject.saveProject()
print "Project saved"
exit()
simConfig.setSimDuration(simDuration)
pm.doGenerate(simConfig.getName(), neuroConstructSeed)
while pm.isGenerating():
print "Waiting for the project to be generated with Simulation Configuration: " + str(simConfig)
if synScaling != 1:
SimulationsInfo.addExtraSimProperty("synapticWeightScaling", str(synScaling))
print "Changing synaptic weight in %s by factor %f"%(netConnName, synScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(0).setWeightsGenerator(NumberGenerator(synScaling))
else:
if gapScaling != 1:
SimulationsInfo.addExtraSimProperty("gapJunctionWeightScaling", str(gapScaling))
print "Changing gap junction weight in %s by factor %f"%(netConnName, gapScaling)
project.morphNetworkConnectionsInfo.getSynapseList(netConnName).get(0).setWeightsGenerator(NumberGenerator(gapScaling))
### Change spatial discretisation of some cells. This will impact accuracy & simulation speed
if maxElecLenFRB > 0:
frbCell = project.cellManager.getCell("L23PyrFRB_varInit")
info = CellTopologyHelper.recompartmentaliseCell(frbCell, maxElecLenFRB, project)
print "Recompartmentalised FRB cell: "+info
if somaNseg > 0:
frbCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenRS > 0:
rsCell = project.cellManager.getCell("L23PyrRS")
info = CellTopologyHelper.recompartmentaliseCell(rsCell, maxElecLenRS, project)
print "Recompartmentalised RS cell: "+info
if somaNseg > 0:
rsCell.getSegmentWithId(0).getSection().setNumberInternalDivisions(somaNseg)
if maxElecLenLTS > 0:
ltsCell = project.cellManager.getCell("SupLTSInter")
info = CellTopologyHelper.recompartmentaliseCell(ltsCell, maxElecLenLTS, project)
print "Recompartmentalised LTS cell: "+info
