copied from https://github.com/wachtlerlab/HB-PAC_disinhibitory_network.git, v1.0

Ai2017Sim.yml

@@ -0,0 +1,74 @@
+name: Ai2017Sim
+channels:
+- brian-team
+- defaults
+dependencies:
+- brian2=2.0.1=np111py35_0
+- py-cpuinfo=0.2.3=py35_1
+- cycler=0.10.0=py35_0
+- cython=0.25.2=py35_0
+- dbus=1.10.10=0
+- decorator=4.0.11=py35_0
+- expat=2.1.0=0
+- fontconfig=2.12.1=3
+- freetype=2.5.5=2
+- glib=2.50.2=1
+- gst-plugins-base=1.8.0=0
+- gstreamer=1.8.0=0
+- icu=54.1=0
+- ipython=5.3.0=py35_0
+- ipython_genutils=0.2.0=py35_0
+- jinja2=2.9.6=py35_0
+- jpeg=9b=0
+- libffi=3.2.1=1
+- libgcc=5.2.0=0
+- libgfortran=3.0.0=1
+- libiconv=1.14=0
+- libpng=1.6.27=0
+- libxcb=1.12=1
+- libxml2=2.9.4=0
+- markupsafe=0.23=py35_2
+- matplotlib=2.0.0=np111py35_0
+- mkl=2017.0.1=0
+- mpmath=0.19=py35_1
+- numpy=1.11.3=py35_0
+- openssl=1.0.2k=1
+- pandas=0.19.2=np111py35_1
+- path.py=10.1=py35_0
+- pcre=8.39=1
+- pexpect=4.2.1=py35_0
+- pickleshare=0.7.4=py35_0
+- pip=9.0.1=py35_1
+- prompt_toolkit=1.0.14=py35_0
+- ptyprocess=0.5.1=py35_0
+- pygments=2.2.0=py35_0
+- pyparsing=2.1.4=py35_0
+- pyqt=5.6.0=py35_2
+- python=3.5.0=1
+- python-dateutil=2.6.0=py35_0
+- pytz=2017.2=py35_0
+- qt=5.6.2=3
+- readline=6.2=2
+- scipy=0.19.0=np111py35_0
+- seaborn=0.7.1=py35_0
+- setuptools=27.2.0=py35_0
+- simplegeneric=0.8.1=py35_1
+- sip=4.18=py35_0
+- six=1.10.0=py35_0
+- sqlite=3.13.0=0
+- sympy=1.0=py35_0
+- tk=8.5.18=0
+- traitlets=4.3.2=py35_0
+- wcwidth=0.1.7=py35_0
+- wheel=0.29.0=py35_0
+- xz=5.0.5=1
+- zlib=1.2.8=3
+- pip:
+  - h5py==2.7.0
+  - ipython-genutils==0.2.0
+  - neo==0.5.0
+  - nixio==1.3.0
+  - prompt-toolkit==1.0.14
+  - quantities==0+unknown
+prefix: /home/ajay/Installations/anaconda3/envs/Ai2017Sim
+

DLInt1SynCurrent.py

@@ -0,0 +1,242 @@
+import os
+import sys
+
+import seaborn as sns
+from brian2 import defaultclock, units, StateMonitor
+from matplotlib import pyplot as plt
+from brian2.core.network import Network
+from dirDefs import homeFolder
+from models.neuronModels import VSNeuron, JOSpikes265, getSineInput
+from models.neurons import AdExp
+from models.synapses import exp2Syn, exp2SynStateInits
+from mplPars import mplPars
+from paramLists import synapsePropsList, inputParsList, AdExpPars
+
+from neo import AnalogSignal
+import nixio
+from neoNIXIO import addAnalogSignal2Block, addMultiTag
+import quantities as qu
+from brianUtils import addBrianQuantity2Section
+
+
+sns.set(style="whitegrid", rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 150 * units.ms
+# inputParsName = 'onePulse'
+# inputParsName = 'twoPulse'
+# inputParsName = 'threePulse'
+inputParsName = "fortyMSPulse"
+showBefore = 300 * units.ms
+showAfter = 50 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 1500 * units.ms
+# # inputParsName = 'oneSecondPulse'
+# # inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# # inputParsName = 'pulseTrainInt33Dur10'
+# # inputParsName = 'pulseTrainInt33Dur16'
+# showBefore = 500 * units.ms
+# showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+DLInt1PropsDict = getattr(AdExpPars, DLInt1ModelProps)
+dlint1 = VSNeuron(**AdExp, inits=DLInt1PropsDict, name='dlint1')
+dlint1.recordMembraneV()
+dlint1.recordSpikes()
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "-".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps, DLInt1SynapseProps, inputParsName)
+
+
+opFile = os.path.join(opDir, 'SynCurrentTraces.png')
+OPNixFile = os.path.join(opDir, 'simResWithSynCurrents.h5')
+if os.path.isfile(opFile):
+    ch = input('Results already exist at {}. Delete?(y/n):'.format(opFile))
+    if ch == 'y':
+        os.remove(opFile)
+        if os.path.isfile(OPNixFile):
+            os.remove(OPNixFile)
+    else:
+        sys.exit('User Abort!')
+
+elif not os.path.isdir(opDir):
+    os.makedirs(opDir)
+
+inputPars = getattr(inputParsList, inputParsName)
+
+JO = JOSpikes265(nOutputs=1, simSettleTime=simSettleTime, **inputPars)
+
+
+if DLInt1SynapsePropsE:
+    synPropsE = getattr(synapsePropsList, DLInt1SynapsePropsE)
+    dlint1.addSynapse(synName="ExiJO", sourceNG=JO.JOSGG, **exp2Syn,
+                      synParsInits=synPropsE,
+                      synStateInits=exp2SynStateInits,
+                      sourceInd=0, destInd=0
+                      )
+
+if DLInt1SynapsePropsI:
+    synPropsI = getattr(synapsePropsList, DLInt1SynapsePropsI)
+    dlint1.addSynapse(synName="InhJO", sourceNG=JO.JOSGG, **exp2Syn,
+                      synParsInits=synPropsI,
+                      synStateInits=exp2SynStateInits,
+                      sourceInd=0, destInd=0
+                      )
+
+net = Network()
+net.add(JO.JOSGG)
+dlint1.addToNetwork(net)
+
+if DLInt1SynapsePropsE:
+    gEMonitor = StateMonitor(dlint1.incomingSynapses["ExiJO"], "g_ExiJO", record=True)
+    net.add(gEMonitor)
+
+if DLInt1SynapsePropsI:
+    gIMonitor = StateMonitor(dlint1.incomingSynapses["InhJO"], "g_InhJO", record=True)
+    net.add(gIMonitor)
+
+
+defaultclock.dt = simStepSize
+totalSimDur = simDuration + simSettleTime
+net.run(totalSimDur, report='text')
+
+simT, memV = dlint1.getMemVTrace()
+spikeTimes = dlint1.getSpikes()
+
+dlint1MemVAS = AnalogSignal(signal=memV / units.mV,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="mV",
+                                name="DLInt1 MemV")
+
+
+dlint1SpikesQU = (spikeTimes / units.ms) * qu.ms
+
+joSpikesQU = (JO.spikeTimes / units.ms) * qu.ms
+
+sineInput = getSineInput(simDur=simDuration, simStepSize=simStepSize,
+                         sinPulseDurs=inputPars['sinPulseDurs'],
+                         sinPulseStarts=inputPars['sinPulseStarts'],
+                         freq=265 * units.Hz, simSettleTime=simSettleTime)
+
+inputAS = AnalogSignal(signal=sineInput,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="um",
+                                name="Input Vibration Signal")
+
+fig, axs = plt.subplots(nrows=3, figsize=(10, 6.25), sharex='col')
+axs[0].plot(simT / units.ms, memV / units.mV)
+axs[0].set_ylabel('DLInt1\nMembrane\nPotential\n(mV)')
+spikesY = memV.min() + 1.05 * (memV.max() - memV.min())
+axs[0].plot(spikeTimes / units.ms, [spikesY / units.mV] * spikeTimes.shape[0], 'k^')
+axs[0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+if DLInt1SynapsePropsE:
+    gSynE = gEMonitor.g_ExiJO[0]
+    iSynE = -gSynE * (memV - synPropsE['Esyn'])
+    axs[1].plot(simT / units.ms,
+                iSynE / units.nA, 'r-', label=r'$I_{synE}$')
+    iSynEAS = AnalogSignal(signal=iSynE / units.nA,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="nA",
+                                name="DL-Int-1 input EPSC")
+
+if DLInt1SynapsePropsI:
+    gSynI = gIMonitor.g_InhJO[0]
+    iSynI = -gSynI * (memV - synPropsI['Esyn'])
+    axs[1].plot(simT / units.ms,
+                iSynI / units.nA, 'g-', label=r'$I_{synI}$')
+    iSynIAS = AnalogSignal(signal=iSynI / units.nA,
+                           sampling_period=(simStepSize / units.ms) * qu.ms,
+                           t_start=0 * qu.mV,
+                           units="nA",
+                           name="DL-Int-1 input IPSC")
+
+axs[1].legend(loc='center right')
+axs[1].set_ylabel("Synaptic\ncurrents\n(nA)")
+
+axs[2].plot(simT / units.ms, sineInput,
+            color=[130. / 255, 72. / 255, 7. / 255], ls='-', marker='None',
+            label='Vibration Input')
+axs[2].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+        label='JO Spikes')
+axs[2].legend(loc='upper right')
+axs[2].set_xlabel('time (ms)')
+axs[2].set_ylabel('Input')
+
+fig.tight_layout()
+fig.canvas.draw()
+fig.savefig(opFile, dpi=150)
+
+nixFile = nixio.File.open(OPNixFile, mode=nixio.FileMode.ReadWrite)
+neuronModels = nixFile.create_section("Neuron Models", "Model Parameters")
+
+DLInt1PropsSec = neuronModels.create_section("DL-Int-1", "AdExp")
+
+for propName, propVal in DLInt1PropsDict.items():
+    addBrianQuantity2Section(DLInt1PropsSec, propName, propVal)
+
+inputSec = nixFile.create_section("Input Parameters", "Sinusoidal Pulses")
+
+for parName, parVal in inputPars.items():
+    addBrianQuantity2Section(inputSec, parName, parVal)
+
+addBrianQuantity2Section(inputSec, "simSettleTime", simSettleTime)
+
+brianSimSettingsSec = nixFile.create_section("Simulation Parameters", "Brian Simulation")
+addBrianQuantity2Section(brianSimSettingsSec, "simStepSize", simStepSize)
+addBrianQuantity2Section(brianSimSettingsSec, "totalSimDuration", totalSimDur)
+brianSimSettingsSec.create_property("method", nixio.Value("euler"))
+
+
+synPropsSec = nixFile.create_section("Synapse Models", "Model Parameters")
+
+if DLInt1SynapsePropsE:
+
+    JODLInt1SynESec = synPropsSec.create_section("JODLInt1Exi", "DoubleExpSyn")
+    JODLInt1SynEDict = getattr(synapsePropsList, DLInt1SynapsePropsE)
+
+    for propName, propVal in JODLInt1SynEDict.items():
+        addBrianQuantity2Section(JODLInt1SynESec, propName, propVal)
+
+    JODLInt1SynESec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+    JODLInt1SynESec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+
+if DLInt1SynapsePropsI:
+
+    JODLInt1SynISec = synPropsSec.create_section("JODLInt1Inh", "DoubleExpSyn")
+    JODLInt1SynIDict = getattr(synapsePropsList, DLInt1SynapsePropsI)
+
+    for propName, propVal in JODLInt1SynIDict.items():
+        addBrianQuantity2Section(JODLInt1SynISec, propName, propVal)
+    JODLInt1SynISec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+    JODLInt1SynISec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+blk = nixFile.create_block("Simulation Traces", "Brian Output")
+DLInt1DA = addAnalogSignal2Block(blk, dlint1MemVAS)
+inputDA = addAnalogSignal2Block(blk, inputAS)
+if DLInt1SynapsePropsE:
+    epscAS = addAnalogSignal2Block(blk, iSynEAS)
+if DLInt1SynapsePropsI:
+    ipscAS = addAnalogSignal2Block(blk, iSynIAS)
+
+addMultiTag("DLInt1 Spikes", type="Spikes", positions=dlint1SpikesQU,
+            blk=blk, refs=[DLInt1DA])
+addMultiTag("JO Spikes", type="Spikes", positions=joSpikesQU,
+            blk=blk, refs=[inputDA])
+
+nixFile.close()

DLInt2try.py

@@ -0,0 +1,81 @@
+import os
+import shutil
+
+import seaborn as sns
+from brian2 import defaultclock, units
+from brian2.core.network import Network
+from matplotlib import pyplot as plt
+
+from dirDefs import homeFolder
+from models.neuronModels import VSNeuron, JOSpikes265, getSineInput
+from mplPars import mplPars
+from paramLists import synapsePropsList, inputParsList
+
+sns.set(style="whitegrid", rc=mplPars)
+
+
+simSettleTime = 500 * units.ms
+
+simStepSize = 0.5 * units.ms
+simDuration = 100 * units.ms
+# inputParsName = 'onePulse'
+# inputParsName = 'twoPulse'
+inputParsName = 'threePulse'
+
+
+# simStepSize = 0.5 * units.ms
+# simDuration = 1100 * units.ms
+# # inputParsName = 'oneSecondPulse'
+# # inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# # inputParsName = 'pulseTrainInt33Dur10'
+# # inputParsName = 'pulseTrainInt33Dur16'
+
+NeuronProps = "DLInt2Try2"
+NeuronSynapseProps = 'DLInt2_syn_try2'
+dlint2 = VSNeuron(NeuronProps)
+
+opDir = os.path.join(homeFolder, NeuronProps, NeuronSynapseProps, inputParsName)
+if os.path.isdir(opDir):
+    ch = input('Results already exist at {}. Delete?(y/n):'.format(opDir))
+    if ch == 'y':
+        shutil.rmtree(opDir)
+os.makedirs(opDir)
+
+period265 = (1 / 265)
+inputPars = getattr(inputParsList, inputParsName)
+JO = JOSpikes265(nOutputs=1, simSettleTime=simSettleTime, **inputPars)
+dlint2.addExp2Synapses(name='JO', nSyn=1, sourceNG=JO.JOSGG,
+                       sourceInd=0,
+                       **getattr(synapsePropsList, NeuronSynapseProps))
+net = Network()
+net.add(JO.JOSGG)
+dlint2.addToNetwork(net)
+defaultclock.dt = simStepSize
+totalSimDur = simDuration + simSettleTime
+net.run(totalSimDur, report='text')
+
+simT, memV = dlint2.getMemVTrace()
+spikeTimes = dlint2.getSpikes()
+fig, axs = plt.subplots(nrows=2, figsize=(10, 6.25), sharex='col')
+axs[0].plot(simT / units.ms, memV / units.mV)
+spikesY = memV.min() + 1.05 * (memV.max() - memV.min())
+axs[0].plot(spikeTimes / units.ms, [spikesY / units.mV] * spikeTimes.shape[0], 'k^')
+axs[0].set_ylabel('DLInt1 \nmemV (mV)')
+axs[0].set_xlim([simSettleTime / units.ms - 50, totalSimDur / units.ms + 50])
+
+sineInput = getSineInput(simSettleTime=simSettleTime, simDur=simDuration,
+                         simStepSize=simStepSize,
+                         sinPulseDurs=inputPars['sinPulseDurs'],
+                         sinPulseStarts=inputPars['sinPulseStarts'],
+                         freq=265 * units.Hz)
+axs[1].plot(simT / units.ms, sineInput, 'r-', label='Vibration Input')
+axs[1].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+        label='JO Spikes')
+axs[1].legend(loc='upper right')
+axs[1].set_xlabel('time (ms)')
+axs[1].set_ylabel('Vibration \nInput/JO\n Spikes')
+fig.tight_layout()
+fig.canvas.draw()
+# plt.show()
+fig.savefig(os.path.join(opDir, 'Traces.png'), dpi=150)

JODLInt1DLInt2.py

@@ -0,0 +1,254 @@
+import os
+import sys
+
+import seaborn as sns
+from brian2 import defaultclock, units
+from brian2.core.network import Network
+from brian2.units.fundamentalunits import Quantity
+from matplotlib import pyplot as plt
+
+from dirDefs import homeFolder
+from models.neuronModels import VSNeuron, JOSpikes265, getSineInput
+from mplPars import mplPars
+from paramLists import synapsePropsList, inputParsList, AdExpPars
+from models.synapses import exp2SynStateInits, exp2Syn
+from models.neurons import AdExp
+
+from neo import AnalogSignal, SpikeTrain
+import nixio
+from neoNIXIO import addAnalogSignal2Block, addMultiTag
+import quantities as qu
+from brianUtils import addBrianQuantity2Section
+
+
+def runJODLInt1DLInt2(simStepSize: Quantity, simDuration: Quantity, simSettleTime: Quantity,
+                      inputParsName: str, showBefore: Quantity, showAfter: Quantity,
+                      DLInt1ModelProps: str, DLInt2ModelProps: str,
+                      DLInt1SynapsePropsE: str, DLInt1SynapsePropsI: str,
+                      DLInt2SynapseProps: str, DLInt1DLInt2SynProps: str,
+                      askReplace=True):
+
+    sns.set(style="whitegrid", rc=mplPars)
+
+    DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+    opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+    opFile = os.path.join(opDir, 'Traces.png')
+    OPNixFile = os.path.join(opDir, 'SimResults.h5')
+
+    if askReplace:
+        if os.path.isfile(opFile):
+            ch = input('Results already exist at {}. Delete?(y/n):'.format(opFile))
+            if ch == 'y':
+                os.remove(opFile)
+                if os.path.isfile(OPNixFile):
+                    os.remove(OPNixFile)
+            else:
+                sys.exit('User Abort!')
+
+        elif not os.path.isdir(opDir):
+            os.makedirs(opDir)
+    else:
+        if os.path.isfile(opFile):
+            os.remove(opFile)
+            if os.path.isfile(OPNixFile):
+                os.remove(OPNixFile)
+
+        elif not os.path.isdir(opDir):
+            os.makedirs(opDir)
+
+    inputPars = getattr(inputParsList, inputParsName)
+
+
+    net = Network()
+    JO = JOSpikes265(nOutputs=1, simSettleTime=simSettleTime, **inputPars)
+    net.add(JO.JOSGG)
+
+    DLInt1PropsDict = getattr(AdExpPars, DLInt1ModelProps)
+    dlint1 = VSNeuron(**AdExp, inits=DLInt1PropsDict, name='dlint1')
+    dlint1.recordSpikes()
+    dlint1.recordMembraneV()
+
+    if DLInt1SynapsePropsE:
+        dlint1.addSynapse(synName="ExiJO", sourceNG=JO.JOSGG, **exp2Syn,
+                              synParsInits=getattr(synapsePropsList, DLInt1SynapsePropsE),
+                              synStateInits=exp2SynStateInits,
+                              sourceInd=0, destInd=0
+                              )
+    if DLInt1SynapsePropsI:
+        dlint1.addSynapse(synName="InhJO", sourceNG=JO.JOSGG, **exp2Syn,
+                              synParsInits=getattr(synapsePropsList, DLInt1SynapsePropsI),
+                              synStateInits=exp2SynStateInits,
+                              sourceInd=0, destInd=0
+                              )
+
+    dlint1.addToNetwork(net)
+
+    DLInt2PropsDict = getattr(AdExpPars, DLInt2ModelProps)
+    dlint2 = VSNeuron(**AdExp, inits=DLInt2PropsDict, name='dlint2')
+    dlint2.recordMembraneV()
+    dlint2.recordSpikes()
+
+    if DLInt2SynapseProps:
+        dlint2.addSynapse(synName="JOExi", sourceNG=JO.JOSGG, **exp2Syn,
+                              synParsInits=getattr(synapsePropsList, DLInt2SynapseProps),
+                              synStateInits=exp2SynStateInits,
+                              sourceInd=0, destInd=0
+                              )
+
+    if DLInt1DLInt2SynProps:
+        dlint2.addSynapse(synName="DLInt1", sourceNG=dlint1.ng, **exp2Syn,
+                              synParsInits=getattr(synapsePropsList, DLInt1DLInt2SynProps),
+                              synStateInits=exp2SynStateInits,
+                              sourceInd=0, destInd=0
+                              )
+
+
+    dlint2.addToNetwork(net)
+    defaultclock.dt = simStepSize
+    totalSimDur = simDuration + simSettleTime
+    net.run(totalSimDur, report='text')
+
+    simT, DLInt1_memV = dlint1.getMemVTrace()
+    DLInt1_spikeTimes = dlint1.getSpikes()
+    fig, axs = plt.subplots(nrows=3, figsize=(10, 6.25), sharex='col')
+    axs[0].plot(simT / units.ms, DLInt1_memV / units.mV)
+    spikesY = DLInt1_memV.min() + 1.05 * (DLInt1_memV.max() - DLInt1_memV.min())
+    axs[0].plot(DLInt1_spikeTimes / units.ms, [spikesY / units.mV] * DLInt1_spikeTimes.shape[0], 'k^')
+    axs[0].set_ylabel('DLInt1 \nmemV (mV)')
+    axs[0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+
+    simT, DLInt2_memV = dlint2.getMemVTrace()
+    DLInt2_spikeTimes = dlint2.getSpikes()
+    axs[1].plot(simT / units.ms, DLInt2_memV / units.mV)
+    spikesY = DLInt2_memV.min() + 1.05 * (DLInt2_memV.max() - DLInt2_memV.min())
+    axs[1].plot(DLInt2_spikeTimes / units.ms, [spikesY / units.mV] * DLInt2_spikeTimes.shape[0], 'k^')
+    axs[1].set_ylabel('DLInt2 \nmemV (mV)')
+
+    sineInput = getSineInput(simDur=simDuration, simStepSize=simStepSize,
+                             sinPulseDurs=inputPars['sinPulseDurs'],
+                             sinPulseStarts=inputPars['sinPulseStarts'],
+                             freq=265 * units.Hz, simSettleTime=simSettleTime)
+    axs[2].plot(simT / units.ms, sineInput, 'r-', label='Vibration Input')
+    axs[2].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+            label='JO Spikes')
+    axs[2].legend(loc='upper right')
+    axs[2].set_xlabel('time (ms)')
+    axs[2].set_ylabel('Vibration \nInput/JO\n Spikes')
+    fig.tight_layout()
+    fig.canvas.draw()
+    fig.savefig(opFile, dpi=150)
+    plt.close(fig.number)
+    del fig
+
+    dlint1MemVAS = AnalogSignal(signal=DLInt1_memV /units.mV,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="mV",
+                                name="DLInt1 MemV")
+    dlint2MemVAS = AnalogSignal(signal=DLInt2_memV / units.mV,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="mV",
+                                name="DLInt2 MemV")
+    inputAS = AnalogSignal(signal=sineInput,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="um",
+                                name="Input Vibration Signal")
+    dlint1SpikesQU = (DLInt1_spikeTimes / units.ms) * qu.ms
+    dlint2SpikesQU = (DLInt2_spikeTimes / units.ms) * qu.ms
+    joSpikesQU = (JO.spikeTimes / units.ms) * qu.ms
+
+    nixFile = nixio.File.open(OPNixFile, mode=nixio.FileMode.ReadWrite)
+
+    neuronModels = nixFile.create_section("Neuron Models", "Model Parameters")
+
+
+    DLInt1PropsSec = neuronModels.create_section("DL-Int-1", "AdExp")
+
+    for propName, propVal in DLInt1PropsDict.items():
+        addBrianQuantity2Section(DLInt1PropsSec, propName, propVal)
+
+    DLInt2PropsSec = neuronModels.create_section("DL-Int-2", "AdExp")
+
+    for propName, propVal in DLInt2PropsDict.items():
+        addBrianQuantity2Section(DLInt2PropsSec, propName, propVal)
+
+    inputSec = nixFile.create_section("Input Parameters", "Sinusoidal Pulses")
+
+    for parName, parVal in inputPars.items():
+        addBrianQuantity2Section(inputSec, parName, parVal)
+
+    addBrianQuantity2Section(inputSec, "simSettleTime", simSettleTime)
+
+    brianSimSettingsSec = nixFile.create_section("Simulation Parameters", "Brian Simulation")
+    addBrianQuantity2Section(brianSimSettingsSec, "simStepSize", simStepSize)
+    addBrianQuantity2Section(brianSimSettingsSec, "totalSimDuration", totalSimDur)
+    brianSimSettingsSec.create_property("method", nixio.Value("euler"))
+
+
+    synPropsSec = nixFile.create_section("Synapse Models", "Model Parameters")
+
+    if DLInt1SynapsePropsE:
+
+        JODLInt1SynESec = synPropsSec.create_section("JODLInt1Exi", "DoubleExpSyn")
+        JODLInt1SynEDict = getattr(synapsePropsList, DLInt1SynapsePropsE)
+
+        for propName, propVal in JODLInt1SynEDict.items():
+            addBrianQuantity2Section(JODLInt1SynESec, propName, propVal)
+
+        JODLInt1SynESec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+        JODLInt1SynESec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+
+    if DLInt1SynapsePropsI:
+
+        JODLInt1SynISec = synPropsSec.create_section("JODLInt1Inh", "DoubleExpSyn")
+        JODLInt1SynIDict = getattr(synapsePropsList, DLInt1SynapsePropsI)
+
+        for propName, propVal in JODLInt1SynIDict.items():
+            addBrianQuantity2Section(JODLInt1SynISec, propName, propVal)
+        JODLInt1SynISec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+        JODLInt1SynISec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+    if DLInt2SynapseProps:
+
+        JODLInt2SynESec = synPropsSec.create_section("JODLInt2Exi", "DoubleExpSyn")
+        JODLInt2SynEDict = getattr(synapsePropsList, DLInt2SynapseProps)
+
+        for propName, propVal in JODLInt2SynEDict.items():
+            addBrianQuantity2Section(JODLInt2SynESec, propName, propVal)
+        JODLInt2SynESec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+        JODLInt2SynESec.create_property("PostSynaptic Neuron", nixio.Value("DLInt2"))
+
+    if DLInt1DLInt2SynProps:
+
+        DLInt1DLInt2SynSec = synPropsSec.create_section("DLInt1DLInt2Inh", "DoubleExpSyn")
+        DLInt1DLInt2SynDict = getattr(synapsePropsList, DLInt1DLInt2SynProps)
+
+        for propName, propVal in DLInt1DLInt2SynDict.items():
+            addBrianQuantity2Section(DLInt1DLInt2SynSec, propName, propVal)
+        DLInt1DLInt2SynSec.create_property("PreSynaptic Neuron", nixio.Value("DLInt1"))
+        DLInt1DLInt2SynSec.create_property("PostSynaptic Neuron", nixio.Value("DLInt2"))
+
+
+    blk = nixFile.create_block("Simulation Traces", "Brian Output")
+    DLInt1DA = addAnalogSignal2Block(blk, dlint1MemVAS)
+    DLInt2DA = addAnalogSignal2Block(blk, dlint2MemVAS)
+    inputDA = addAnalogSignal2Block(blk, inputAS)
+    addMultiTag("DLInt1 Spikes", type="Spikes", positions=dlint1SpikesQU,
+                blk=blk, refs=[DLInt1DA])
+    addMultiTag("DLInt2 Spikes", type="Spikes", positions=dlint2SpikesQU,
+                blk=blk, refs=[DLInt2DA])
+    addMultiTag("JO Spikes", type="Spikes", positions=joSpikesQU,
+                blk=blk, refs=[inputDA])
+
+
+    nixFile.close()
+
+
+

LICENSE-CCBY

@@ -0,0 +1,99 @@
+Creative Commons Attribution 4.0 International Public License
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Readme.md

@@ -0,0 +1,73 @@
+This repository contains the code used for the following manuscript:
+
+
+Kumaraswamy, A., Maksutov, A., Kai, K., Ai, H., Ikeno, H., & Wachtler, T. (2017). Network simulations of interneuron circuits in the honeybee primary auditory center. *bioRxiv*. https://doi.org/10.1101/159533
+
+Authors:
+Ajayrama Kumaraswamy, ajkumaraswamy@tutamail.com
+Based on and contains parts of work by Aynur Maksutov during AMGEN program 2016 at Wachtlerlab, LMU.
+
+Installation:
+
+With anaconda (recommended):
+
+    1. conda create --name Ai2017Sim -c brian-team ipython>=6.1 numpy>=1.11.2 matplotlib>=1.5.3 seaborn>=0.7.1 brian2>=2.0.1 python>=3.5
+    2. source activate Ai2017Sim (unix) or activate Ai2017Sim (windows)
+    3. pip install <full path of this repository>
+
+without anaconda, normal python installation required (https://www.python.org/)
+
+    1. Install virtualenvwrapper (unix) or virtualenvwrapper-win (windows) be pre-installed with pip
+    2. (only on windows) Install microsoft Visual C++ 14.0. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
+    3. mkvirtualenv Ai2017Sim
+    4. pip install <full path of this repository>
+
+Usage:
+    1. source activate Ai2017Sim (unix) or activate Ai2017Sim (windows)
+    2. Change the variable homeFolder in dirDefs.py to a folder. The results of the simulation will be stored here.
+    3. The scripts of this repo are described below. All of them have some parameters at their top. Change these and run the scripts as needed.
+
+
+Here is an overview of the contents:
+
+.  
++-- Ai2017Sim.yml: A file that can be used to create a conda environment to run the scripts below. Essentially is a list of dependencies.
++-- models
+|   +-- neuronModels.py: wrapper classes for brian2 neuron models
+|   +-- neurons.py: Model equations and static parameters for neurons  
+|   +-- synapses.py: Model equations for synapses  
+|  
++-- paramLists  
+|   +-- AdExpPars.py: Parameter combinations for the AdExp model  
+|   +-- inputParsList.py: Stimulii definitions  
+|   +-- synapsePropsList.py: Parameter combinations for the difference of exponential synaptic conductance model  
+|  
++-- brianUtils.py: utility function related to brian2  
+|  
++-- dirDefs.py: directory definitions imported in other scripts  
+|  
++-- DLInt1SynCurrent.py: Script to simulate DL-Int-1 recording membrane potential and synaptic currents in [NIX](https://github.com/G-Node/nixpy) files  
+|  
++-- DLInt2try.py: Legacy code  
+|  
++-- forAi2017.py: Script to generate a subplot of an upcoming manuscript.  
+|  
++-- JODLInt1DLInt2: Class to run network simulations  
+|  
++-- justDLInt1.py: Legacy code  
+|  
++-- mplPars.py: matplotlib rc parameters  
+|  
++-- neoNIXIO.py: adapted from GJEMS, utility functions to work jointly with [NIX](https://github.com/G-Node/nixpy) and [neo](https://github.com/NeuralEnsemble/python-neo).  
+|  
++-- plotDLInt1DLInt2SynEffects.py: script to plot summary of DL-Int-1 and DL-Int-2 responses to pulse trains.  
+|  
++-- plotShortStims.py: script to plot summary of DL-Int-1 and DL-Int-2 responses to short continuous pulses.  
+|  
++-- plotSynCurrents.py: script to plot membrane potential and synaptic currents of DL-Int-1 and DL-Int-2 for one stimulus.  
+|  
++-- runJODLInt1DLInt2Multiple.py: script to simulate the network for multiple stimulii. Out is saved as a [NIX](https://github.com/G-Node/nixpy) File.  
+|  
++-- simSynCurrents.py: script to simulate DL-Int-1 and DL-Int-2 recording membrane potential and synaptics currents in a [NIX](https://github.com/G-Node/nixpy) file.  
+
+

brianUtils.py

@@ -0,0 +1,31 @@
+import numpy as np
+from brian2.units.fundamentalunits import Quantity
+import nixio
+
+def getSimT(simDur: Quantity, simStepSize: Quantity) -> Quantity:
+
+    return np.arange(simDur / simStepSize) * simStepSize
+
+def addBrianQuantity2Section(sec: nixio.pycore.Section,
+                             name: str, qu: Quantity) -> nixio.pycore.Property:
+    propStr = qu.in_best_unit()
+
+    if qu.shape == ():
+
+        propFloatStr, propUnit = propStr.split(" ")
+        propFloat = float(propFloatStr)
+
+        pr = sec.create_property(name, [nixio.Value(propFloat)])
+
+    elif len(qu.shape) == 1:
+
+        propFloatStr, propUnit = propStr.split("] ")
+        values = list(map(float, propFloatStr[2:].split()))
+        pr = sec.create_property(name, [nixio.Value(val) for val in values])
+
+    else:
+        raise(ValueError("Only scalar or 1D Brian Quantities as supported"))
+
+    pr.unit = propUnit
+
+    return pr

butest.py

@@ -0,0 +1,7 @@
+import nixio
+from brian2 import units
+from brianUtils import addBrianQuantity2Section
+
+nf = nixio.File.open("/tmp/1.h5", nixio.FileMode.ReadWrite)
+sec = nf.create_section("test", "Test")
+addBrianQuantity2Section(sec, "Test", [1] * units.ms)

dirDefs.py

@@ -0,0 +1,3 @@
+import os
+homeFolder = os.path.join('/media', 'ajay', 'ADATA_HD720', 'Ginjang',
+                          'DataAndResults', 'ephys', 'Ai2017Sim')

forAi2017.py

@@ -0,0 +1,150 @@
+import nixio
+from dirDefs import homeFolder
+import os
+import seaborn as sns
+from mplPars import mplPars
+from brian2 import units
+from matplotlib import pyplot as plt
+from neoNIXIO import multiTag2SpikeTrain, dataArray2AnalogSignal, \
+    simpleFloat, property2qu
+import quantities as qu
+from neo import AnalogSignal
+
+mplPars['xtick.labelsize'] = 10
+mplPars['ytick.labelsize'] = 10
+mplPars["text.usetex"] = False
+mplPars["font.sans-serif"] = "Arial"
+mplPars["axes.linewidth"] = 1
+sns.set(style="ticks", rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 150 * units.ms
+# # inputParsName = 'onePulse'
+# # inputParsName = 'twoPulse'
+# # inputParsName = 'threePulse'
+# inputParsName = "fortyMSPulse"
+# showBefore = 50 * units.ms
+# showAfter = 50 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 450 * units.ms
+inputParsName = "pTShortInt33Dur16"
+# inputParsName = "pTShortInt100Dur16"
+
+showBefore = 75 * units.ms
+showAfter = -30 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 1500 * units.ms
+# # inputParsName = 'oneSecondPulse'
+# # inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# # inputParsName = 'pulseTrainInt33Dur10'
+# # inputParsName = 'pulseTrainInt33Dur16'
+# showBefore = 500 * units.ms
+# showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+DLInt2ModelProps = "DLInt2Try2"
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+OPNixFile = os.path.join(opDir, 'simResults.h5')
+
+
+totalSimDur = simDuration + simSettleTime
+
+nixFile = nixio.File.open(OPNixFile, nixio.FileMode.ReadOnly)
+
+inputSec = nixFile.sections["Input Parameters"]
+simSettleTimeQu = property2qu(inputSec.props["simSettleTime"])
+
+blk = nixFile.blocks["Simulation Traces"]
+dlint1MemV = blk.data_arrays["DLInt1 MemV"]
+dlint1SpikesMT = blk.multi_tags["DLInt1 Spikes"]
+dlint2MemV = blk.data_arrays["DLInt2 MemV"]
+dlint2SpikesMT = blk.multi_tags["DLInt2 Spikes"]
+sinInput = blk.data_arrays["Input Vibration Signal"]
+joSpikesMT = blk.multi_tags["JO Spikes"]
+
+dlint1MemVAS = dataArray2AnalogSignal(dlint1MemV)
+dlint2MemVAS = dataArray2AnalogSignal(dlint2MemV)
+temp = dataArray2AnalogSignal(sinInput)
+sinInputAS = AnalogSignal(signal=15 * temp.magnitude,
+                          units=temp.units,
+                          t_start=temp.t_start,
+                          sampling_period=temp.sampling_period)
+sinInputAS = sinInputAS.reshape((sinInputAS.shape[0],))
+dlint1SpikesST = multiTag2SpikeTrain(dlint1SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+dlint2SpikesST = multiTag2SpikeTrain(dlint2SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+joSpikesST = multiTag2SpikeTrain(joSpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+
+# fig0, ax0 = plt.subplots(figsize=(2.5, 1.5))
+# fig1, ax1 = plt.subplots(figsize=(2.5, 1.5))
+# fig2, ax2 = plt.subplots(figsize=(2.5, 1.75))
+#
+fig0, ax0 = plt.subplots(figsize=(2.85, 1.5))
+fig1, ax1 = plt.subplots(figsize=(2.85, 1.5))
+fig2, ax2 = plt.subplots(figsize=(2.85, 1.75))
+
+ax0.plot(simpleFloat((dlint1MemVAS.times - simSettleTimeQu) / qu.ms),
+         simpleFloat(dlint1MemVAS / qu.mV), 'k-', lw=0.4)
+ax1.plot(simpleFloat((dlint2MemVAS.times - simSettleTimeQu) / qu.ms),
+         simpleFloat(dlint2MemVAS / qu.mV), 'k-', lw=0.4)
+ax2.plot(simpleFloat((sinInputAS.times - simSettleTimeQu) / qu.ms),
+         simpleFloat(sinInputAS / qu.um), 'k-', lw=0.4)
+
+for ax in [ax0, ax1, ax2]:
+    ax.set_xlim([(-showBefore) / units.ms,
+                     (simDuration + showAfter) / units.ms])
+    # ax.yaxis.tick_right()
+    ax.set_xticks([])
+    ax.set_ylim([-50, 5])
+    ax.set_yticks([-40, -20, 0])
+    # ax.set_yticks([])
+
+
+for ax in [ax0, ax1]:
+    markerline, stemlines, baseline \
+        = ax.stem(simpleFloat((joSpikesST.times - simSettleTimeQu) / qu.ms),
+                      [-42] * joSpikesST.shape[0],
+                      linefmt='k-', markerfmt='None', basefmt='None',
+                      bottom=-50)
+    plt.setp(stemlines, lw=0.4)
+
+
+markerline, stemlines, baseline \
+        = ax2.stem(simpleFloat((joSpikesST.times - simSettleTimeQu) / qu.ms),
+                      [25] * joSpikesST.shape[0],
+                      linefmt='k-', markerfmt='None', basefmt='None',
+                      bottom=17)
+plt.setp(stemlines, lw=0.5)
+ax2.set_ylim([-20, 25])
+ax2.set_xticks([0, 100, 200, 300])
+
+for fig in [fig0, fig1, fig2]:
+
+    fig.tight_layout()
+
+fig0.savefig(os.path.join(opDir, "DL-Int-1MemV.svg"), dpi=300,
+             bbox_inches='tight', transparent=True)
+fig1.savefig(os.path.join(opDir, "DL-Int-2MemV.svg"), dpi=300,
+             bbox_inches='tight', transparent=True)
+fig2.savefig(os.path.join(opDir, "InputSignal.svg"), dpi=300,
+             bbox_inches='tight', transparent=True)
+

justDLInt1.py

@@ -0,0 +1,130 @@
+import os
+import sys
+
+import seaborn as sns
+from brian2 import defaultclock, units
+from matplotlib import pyplot as plt
+from brian2.core.network import Network
+from dirDefs import homeFolder
+from models.neuronModels import VSNeuron, JOSpikes265, getSineInput
+from models.neurons import AdExp
+from models.synapses import exp2Syn, exp2SynStateInits
+from mplPars import mplPars
+from paramLists import synapsePropsList, inputParsList, AdExpPars
+
+sns.set(style="whitegrid", rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 150 * units.ms
+# # inputParsName = 'onePulse'
+# # inputParsName = 'twoPulse'
+# # inputParsName = 'threePulse'
+# # inputParsName = "tenMSPulse"
+# # inputParsName = "twentyMSPulse"
+# # inputParsName = "thirtyMSPulse"
+# inputParsName = "fortyMSPulse"
+# # inputParsName = "fiftyMSPulse"
+# showBefore = 50 * units.ms
+# showAfter = 10 * units.ms
+
+
+simStepSize = 0.1 * units.ms
+simDuration = 1500 * units.ms
+# inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# inputParsName = 'pulseTrainInt33Dur16'
+# inputParsName = 'pulseTrainInt33Dur10'
+# inputParsName = 'oneSecondPulse'
+# inputParsName = 'pulseTrainInt50Dur10'
+inputParsName = 'pulseTrainInt50Dur16'
+# inputParsName = 'pulseTrainInt50Dur20'
+showBefore = 500 * units.ms
+showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+dlint1 = VSNeuron(**AdExp, inits=getattr(AdExpPars, DLInt1ModelProps), name='dlint1')
+dlint1.recordMembraneV()
+dlint1.recordSpikes()
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "-".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+
+# opDir = os.path.join(homeFolder, DLInt1ModelProps, DLInt1SynapseProps, inputParsName)
+opDir = "/tmp/justDLInt1"
+
+opFile = os.path.join(opDir, 'Traces.png')
+if os.path.isfile(opFile):
+    ch = input('Results already exist at {}. Delete?(y/n):'.format(opFile))
+    if ch == 'y':
+        os.remove(opFile)
+    else:
+        sys.exit('User Abort!')
+
+elif not os.path.isdir(opDir):
+    os.makedirs(opDir)
+
+inputPars = getattr(inputParsList, inputParsName)
+
+JO = JOSpikes265(nOutputs=1, simSettleTime=simSettleTime, **inputPars)
+
+
+if DLInt1SynapsePropsE:
+    dlint1.addSynapse(synName="ExiJO", sourceNG=JO.JOSGG, **exp2Syn,
+                      synParsInits=getattr(synapsePropsList, DLInt1SynapsePropsE),
+                      synStateInits=exp2SynStateInits,
+                      sourceInd=0, destInd=0
+                      )
+if DLInt1SynapsePropsI:
+    dlint1.addSynapse(synName="InhJO", sourceNG=JO.JOSGG, **exp2Syn,
+                      synParsInits=getattr(synapsePropsList, DLInt1SynapsePropsI),
+                      synStateInits=exp2SynStateInits,
+                      sourceInd=0, destInd=0
+                      )
+
+net = Network()
+net.add(JO.JOSGG)
+dlint1.addToNetwork(net)
+defaultclock.dt = simStepSize
+totalSimDur = simDuration + simSettleTime
+net.run(totalSimDur, report='text')
+
+simT, memV = dlint1.getMemVTrace()
+spikeTimes = dlint1.getSpikes()
+
+
+fig, axs = plt.subplots(nrows=2, figsize=(10, 6.25), sharex='col')
+axs[0].plot(simT / units.ms, memV / units.mV)
+spikesY = memV.min() + 1.05 * (memV.max() - memV.min())
+axs[0].plot(spikeTimes / units.ms, [spikesY / units.mV] * spikeTimes.shape[0], 'k^')
+axs[0].set_ylabel('DLInt1 \nmemV (mV)')
+axs[0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                 (totalSimDur + showAfter) / units.ms])
+sineInput = getSineInput(simDur=simDuration, simStepSize=simStepSize,
+                         sinPulseDurs=inputPars['sinPulseDurs'],
+                         sinPulseStarts=inputPars['sinPulseStarts'],
+                         freq=265 * units.Hz, simSettleTime=simSettleTime)
+axs[1].plot(simT / units.ms, sineInput, 'r-', label='Vibration Input')
+axs[1].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+        label='JO Spikes')
+axs[1].legend(loc='upper right')
+axs[1].set_xlabel('time (ms)')
+axs[1].set_ylabel('Vibration \nInput/JO\n Spikes')
+fig.tight_layout()
+fig.canvas.draw()
+# plt.show()
+fig.savefig(opFile, dpi=150)
+
+
+
+
+
+
+
+

models/neuronModels.py

@@ -0,0 +1,247 @@
+from copy import copy
+from typing import Union, Iterable
+
+import numpy as np
+from brian2 import NeuronGroup, TimedArray, StateMonitor, SpikeMonitor, SpikeGeneratorGroup, array
+from brian2 import Synapses
+from brian2 import units
+from brian2.core.network import Network
+from brian2.equations.codestrings import CodeString
+from brian2.equations.equations import Equations
+from brian2.units.fundamentalunits import Quantity
+
+from brianUtils import getSimT
+
+def addSynNameVar(var: str, name: str) -> str:
+
+    return "_".join([var, name])
+
+def addSynNameEqs(model: str, prePosts: Iterable[Union[str, None]], synName: str) -> tuple:
+
+    mEq = Equations(model)
+    newM = copy(model)
+    for name in mEq.names:
+        if (not name.endswith("_post")) and (not name.endswith("_pre")):
+            newM = newM.replace(name, addSynNameVar(name, synName))
+
+
+    newPrePosts = []
+    prePostCSs = []
+    for p in prePosts:
+        newP = copy(p)
+        if p:
+            cs = CodeString(p)
+            for name in cs.identifiers:
+                if (not name.endswith("_post")) and (not name.endswith("_pre")):
+                    newP = newP.replace(name, addSynNameVar(name, synName))
+            newPrePosts.append(newP)
+            prePostCSs.append(cs)
+        else:
+            newPrePosts.append(None)
+            prePostCSs.append(None)
+
+    return newM, mEq, newPrePosts, prePostCSs
+
+
+class VSNeuron(object):
+
+    def __init__(self, model: str, name: str,
+                 inits: dict,
+                 threshold: str,
+                 reset: str,
+                 method: str = "euler"):
+
+        super().__init__()
+        self.ngParams = {"model": model, "threshold": threshold, "reset": reset, "method": method,
+                         "name": name}
+        self.inits = inits
+        self.incomingSynapses = {}
+        self.incomingSynapsePars = {}
+        self.synCurrentNames = []
+        self.recordMemVFlag = False
+        self.recordSpikesFlag = False
+        self.ng = None
+
+    def updateInits(self, initUpdate: dict):
+
+        self.inits.update(initUpdate)
+
+    def setInputCurrent(self, I: Union[TimedArray, float]):
+        self.inits["I"] = I
+
+    def recordMembraneV(self):
+
+        self.recordMemVFlag = True
+
+    def recordSpikes(self):
+
+        self.recordSpikesFlag = True
+
+    def getMemVTrace(self):
+
+        assert self.recordMemVFlag, 'Membrane Voltage was not recorded' \
+                                            'for this neuron'
+        return self.memVRecord.t, self.memVRecord[0].V
+
+    def getSpikes(self):
+
+        assert self.recordSpikesFlag, "Spikes were not recorded for this neuron"
+
+        return self.spikeRecord.t
+
+    def addToNetwork(self, network: Network):
+
+        self.ngParams["model"] = "\n".join((self.ngParams["model"], "Iext: amp"))
+        self.inits["Iext"] = 0 * units.amp
+        eq2Add = "I = Iext "
+
+        for synCurrentName in self.synCurrentNames:
+
+            self.ngParams["model"] = "\n".join((self.ngParams["model"], "{} : amp".format(synCurrentName)))
+            self.inits[synCurrentName] = 0 * units.amp
+            eq2Add += " + {} ".format(synCurrentName)
+
+        eq2Add += ": amp"
+
+        self.ngParams["model"] = "\n".join((self.ngParams["model"], eq2Add))
+
+        self.ng = NeuronGroup(N=1, **self.ngParams)
+        self.initSim()
+        network.add(self.ng)
+        if self.recordMemVFlag:
+            self.memVRecord = StateMonitor(self.ng, "V", record=[0])
+            network.add(self.memVRecord)
+        if self.recordSpikesFlag:
+            self.spikeRecord = SpikeMonitor(self.ng)
+            network.add(self.spikeRecord)
+
+        for synName, synPars in self.incomingSynapsePars.items():
+
+            syn = Synapses(synPars["source"], self.ng,
+                           model=synPars["model"],
+                           on_pre=synPars["on_pre"],
+                           on_post=synPars["on_post"],
+                           method=synPars["method"])
+
+            syn.connect(i=synPars["sourceInd"], j=synPars["destInd"])
+
+            for k, v in synPars["initMap"].items():
+
+                setattr(syn, k, v)
+
+            self.incomingSynapses[synName] = syn
+            network.add(syn)
+
+
+    def initSim(self):
+
+        for k, v in self.inits.items():
+            setattr(self.ng, k, v)
+
+    def addSynapse(self, synName: str, sourceNG: NeuronGroup,
+                   model: str, synParsInits: dict, synStateInits: dict,
+                   on_pre: Union[str, None] = None,
+                   on_post: Union[str, None] = None,
+                   sourceInd: int = 0, destInd: int = 0,
+                   method: str = "euler"):
+
+
+        assert synName not in self.incomingSynapses, 'A Synapse with {} already exists'.format(synName)
+        ISyn_PostInd = model.find("ISyn_post")
+        assert ISyn_PostInd >= 0, "Synapse model should have an equation for" \
+                                            "\'ISyn_post\'"
+        nextEndLineInd = model.find("\n", ISyn_PostInd)
+        assert model[nextEndLineInd - 8: nextEndLineInd] == "(summed)", \
+            "Equation for \'ISyn_post\' must have (summed) flag"
+
+
+        newModel, mEq, [newOn_pre, newOn_post], prePostCSs = \
+            addSynNameEqs(model, [on_pre, on_post], synName)
+
+        allSV = mEq.diff_eq_names
+        allPars = list(mEq.parameter_names)
+
+
+        for cs in prePostCSs:
+            if cs:
+                for i in cs.identifiers:
+                    if i not in allSV:
+                        allPars.append(i)
+
+        for par in allPars:
+            assert par in synParsInits, "Initialization not provided for {} in synParsInits".format(par)
+
+        for sv in allSV:
+            assert sv in synStateInits, "Initialization not provided for {} in synStateInits".format(sv)
+
+        ISynName = "_".join(("ISyn", synName))
+        self.synCurrentNames.append(ISynName)
+
+        newModel = newModel.replace("ISyn", ISynName)
+
+        initMap = {"delay": synParsInits["delay"]}
+        for par in allPars:
+            initMap[addSynNameVar(par, synName)] = synParsInits[par]
+
+        for sv in allSV:
+            initMap[addSynNameVar(sv, synName)] = synStateInits[sv]
+
+        synPars = {"source": sourceNG, "model": newModel, "on_pre": newOn_pre,
+                   "on_post": newOn_post, "method": method,
+                   "sourceInd": sourceInd, "destInd": destInd, "initMap": initMap}
+
+        self.incomingSynapsePars[synName] = synPars
+
+
+
+
+class JOSpikes265(object):
+
+    def __init__(self, nOutputs: int =1, simSettleTime: Quantity = 0 * units.ms,
+                 sinPulseStarts: array = array(()) * units.ms,
+                 sinPulseDurs: array = array(()) * units.ms):
+
+        self.nOutputs = nOutputs
+        freq = 265 * units.Hz
+        spikePhase = np.deg2rad(240)
+        phaseDelay = (1 / freq) * (spikePhase / (2 * np.pi))
+        self.spikeTimes = []
+        self.spikeInds = []
+        simSettleTimeF = float(simSettleTime)
+
+        for start, dur in zip(sinPulseStarts, sinPulseDurs):
+
+            startF = float(start)
+            durF = float(dur)
+            periodF = float(1/freq)
+            phaseDelayF = float(phaseDelay)
+
+            cycleStarts = np.arange(startF, startF + durF, periodF)
+            for i in range(nOutputs):
+                self.spikeTimes += (simSettleTimeF + cycleStarts + phaseDelayF).tolist()
+                self.spikeInds += [i] * len(cycleStarts)
+
+        self.spikeTimes = self.spikeTimes * units.second
+        self.JOSGG = SpikeGeneratorGroup(nOutputs, array(self.spikeInds),
+                                         self.spikeTimes)
+
+def getSineInput(simDur: Quantity, simStepSize: Quantity,
+                 sinPulseStarts: Quantity, sinPulseDurs: Quantity,
+                 freq: Quantity, simSettleTime: Quantity = 0 * units.ms,):
+
+    simT = getSimT(simSettleTime + simDur, simStepSize)
+    sineInput = np.zeros(simT.shape)
+
+    for start, dur in zip(sinPulseStarts, sinPulseDurs):
+
+        settleStart = start + simSettleTime
+        settleEnd = start + dur + simSettleTime
+
+        timeMask = (simT >= settleStart) & (simT <= settleEnd)
+        sineInput[timeMask] = np.sin(2 * np.pi * freq * (simT[timeMask] - (0.5 / freq) - start))
+
+    return sineInput
+
+
+
+

models/neurons.py

@@ -0,0 +1,20 @@
+AdExpEqs = "\n".join((
+    "Ex = gL*sF*exp( (V - Vt)/sF ) : amp",
+    "IL = gL*(EL - V) : amp",
+    "dV/dt = (I + IL + Ex - w)/C : volt",
+    "dw/dt = (a*(V - EL) - w)/tau : amp",
+    "Vt : volt",
+    "Vr : volt",
+    "b : amp",
+    "sF : volt",
+    "tau: second",
+    "EL : volt",
+    "gL : siemens",
+    "C : farad",
+    "a : siemens",
+    "Vp : volt"
+    ))
+
+AdExp = {"model": AdExpEqs,
+         "threshold": "V > Vp",
+         "reset": "V = Vr; w+=b"}

models/synapses.py

@@ -0,0 +1,22 @@
+from brian2 import units
+
+exp2SynEqs = "\n".join((
+    "g = B - A: siemens",
+    "ISyn_post =  -g * (V_post - Esyn): amp (summed)",
+    "dB/dt = -B/tau2: siemens (clock-driven)",
+    "dA/dt = -A/tau1: siemens (clock-driven)",
+    "tau1: second",
+    "tau2: second",
+    "wSyn: siemens",
+    "Esyn: volt",
+    ))
+
+exp2Syn = {
+    "model": exp2SynEqs,
+    "on_pre": "A += wSyn\nB += wSyn",
+}
+
+exp2SynStateInits = {
+    "A": 0 * units.siemens,
+    "B": 0 * units.siemens
+}

mplPars.py

@@ -0,0 +1,29 @@
+# mplPars = {'text.usetex': True,
+#            'axes.labelsize': 'large',
+#            'axes.titlesize': 24,
+#            'font.family': 'sans-serif',
+#            'font.sans-serif': 'computer modern roman',
+#            'font.size': 24,
+#            'font.weight': 'black',
+#            'xtick.labelsize': 20,
+#            'ytick.labelsize': 20,
+#            'legend.fontsize': 20,
+#            'legend.frameon': True,
+#            'legend.framealpha': 0,
+#            'legend.fancybox': True,
+#            'text.latex.preamble': r'\usepackage{cmbright}'
+#            }
+
+mplPars = {'axes.labelsize': 'large',
+           'axes.titlesize': 24,
+           'font.family': 'sans-serif',
+           'font.sans-serif': 'computer modern roman',
+           'font.size': 24,
+           'font.weight': 'black',
+           'xtick.labelsize': 20,
+           'ytick.labelsize': 20,
+           'legend.fontsize': 20,
+           'legend.frameon': True,
+           'legend.framealpha': 0,
+           'legend.fancybox': True,
+           }

neoNIXIO.py

@@ -0,0 +1,316 @@
+# Ajayrama Kumaraswamy, 2016
+# Ginjang Project, LMU
+
+import nixio as nix
+import neo
+import quantities as qu
+import numpy as np
+
+qu2Val = lambda x: nix.Value(float(x))
+quUnitStr = lambda x: x.dimensionality.string
+
+#***********************************************************************************************************************
+
+def addAnalogSignal2Block(blk, analogSignal):
+    '''
+    Create a new data array in the block blk and add the data in analogSignal to it
+    :param blk: nix.block
+    :param analogSignal: neo.analogsignal
+    :return: data, nix.data_array, the newly added data_array
+    '''
+
+    assert hasattr(analogSignal, 'name'), 'Analog signal has no name'
+
+    data = blk.create_data_array(analogSignal.name, 'nix.regular_sampled', data=analogSignal.magnitude)
+
+    data.unit = quUnitStr(analogSignal)
+    data.label = analogSignal.name
+
+    qu.set_default_units = 'SI'
+    samplingPeriod = analogSignal.sampling_period.simplified
+    t = data.append_sampled_dimension(float(samplingPeriod))
+    t.label = 'time'
+    t.unit = quUnitStr(samplingPeriod)
+    t.offset = float(analogSignal.t_start.simplified)
+
+    return data
+
+#***********************************************************************************************************************
+
+def dataArray2AnalogSignal(dataArray):
+    '''
+    Convert a nix data_array into a neo analogsignal
+    :param dataArray: nix.data_array
+    :return: neo.analogsignal
+    '''
+
+    assert len(dataArray.dimensions) == 1, 'Only one dimensional arrays are supported'
+    dim = dataArray.dimensions[0]
+    assert isinstance(dim, nix.pycore.SampledDimension), 'Only Sampled Dimensions' \
+                                                         'are supported'
+
+    t_start = qu.Quantity(dim.offset, units=dim.unit)
+    samplingPeriod = qu.Quantity(dim.sampling_interval, units=dim.unit)
+
+    analogSignal = neo.AnalogSignal(signal=np.array(dataArray[:]),
+                                    units=dataArray.unit,
+                                    sampling_period=samplingPeriod,
+                                    t_start=t_start)
+
+    analogSignal.name = dataArray.name
+
+    return analogSignal
+
+#***********************************************************************************************************************
+
+def property2qu(property):
+    '''
+    Convert a nix property to a quantities Quantity
+    :param property: nix.property
+    :return: quantities.Quantity
+    '''
+
+    return qu.Quantity([v.value for v in property.values], units=property.unit)
+
+#***********************************************************************************************************************
+
+def addQuantity2section(sec, quant, name):
+    '''
+    Create new property in section sec and add the data in quantity.Quantitiy quant to it
+    :param sec: nix.section
+    :param quant: quantities.Quantity
+    :param name: name of the property to add
+    :return: p, nix.property, the property added.
+    '''
+
+    if quant.shape == ():
+
+        p = sec.create_property(name, [qu2Val(quant)])
+
+    #only 1D arrays
+    elif len(quant.shape) == 1:
+
+        #not an empty 1D array
+        if quant.shape[0]:
+
+            p = sec.create_property(name, [qu2Val(x) for x in quant])
+
+        else:
+            raise(ValueError('Quantity passed must be either scalar or 1 dimensional'))
+
+    else:
+            raise(ValueError('Quantity passed must be either scalar or 1 dimensional'))
+
+    p.unit = quUnitStr(quant)
+
+    return p
+
+#***********************************************************************************************************************
+
+def createPosDA(name, pos, blk):
+    '''
+    Create a data_array of type 'nix.positions' with the pos data in the block blk
+    :param name: string, name of the data_array to create
+    :param pos: iterable of floats, data to be added to the created data_array
+    :param blk: nix.block, the block in which the data_array is to be created
+    :return: positions, nix.data_array, the newly created data_array
+    '''
+
+    positions = blk.create_data_array(name, 'nix.positions', data=pos)
+    positions.append_set_dimension()
+    positions.append_set_dimension()
+
+    return positions
+
+#***********************************************************************************************************************
+
+def createExtDA(name, ext, blk):
+    '''
+   Create a data_array of type 'nix.extents' with the pos data in the block blk
+   :param name: string, name of the data_array to create
+   :param ext: iterable of floats, data to be added to the created data_array
+   :param blk: nix.block, the block in which the data_array is to be created
+   :return: extents, nix.data_array, the newly created data_array
+   '''
+
+    extents = blk.create_data_array(name, 'nix.extents', data=ext)
+    extents.append_set_dimension()
+    extents.append_set_dimension()
+
+    return extents
+
+#***********************************************************************************************************************
+
+def tag2AnalogSignal(tag, refInd):
+    '''
+    Create a neo.analogsignal from the snippet of data represented by a nix.tag and its reference at index refInd
+    :param tag: nix.tag
+    :param refInd: the index of the reference among those of the tag to use
+    :return: neo.analogsignal with the snipped of reference data tagged by tag.
+    '''
+
+    ref = tag.references[refInd]
+    dim = ref.dimensions[0]
+    offset = dim.offset
+    ts = dim.sampling_interval
+    nSamples = ref[:].shape[0]
+
+    startInd = max(0, int(np.floor((tag.position[0] - offset) / ts)))
+    endInd = min(startInd + int(np.floor(tag.extent[0] / ts)) + 1, nSamples)
+    trace = ref[startInd:endInd]
+
+    analogSignal = neo.AnalogSignal(signal=trace,
+                                    units=ref.unit,
+                                    sampling_period=qu.Quantity(ts, units=dim.unit),
+                                    t_start=qu.Quantity(offset + startInd * ts, units=dim.unit))
+
+    analogSignal = analogSignal.reshape((analogSignal.shape[0],))
+    # trace = tag.retrieve_data(refInd)[:]
+    # tVec = tag.position[0] + np.linspace(0, tag.extent[0], trace.shape[0])
+
+    return analogSignal
+
+#***********************************************************************************************************************
+
+def getTagPosExt(tag):
+
+    position = tag.position[0] * qu.Quantity(1, units=tag.units[0])
+    extent = tag.extent[0] * qu.Quantity(1, units=tag.units[0])
+
+    return position, extent
+
+
+
+#***********************************************************************************************************************
+def multiTag2SpikeTrain(tag, tStart, tStop):
+    '''
+    Create a neo.spiketrain from nix.multitag
+    :param tag: nix.multitag
+    :param tStart: float, time of start of the spike train in units of the multitag
+    :param tStop: float, time of stop of the spike train in units of the multitag
+    :return: neo.spiketrain
+    '''
+
+
+    if len(tag.positions):
+        sp = neo.SpikeTrain(times=tag.positions[:], t_start=tStart, t_stop=tStop, units=tag.units[0])
+    else:
+        sp = neo.SpikeTrain(times=[], t_start=tStart, t_stop=tStop, units=qu.s)
+
+    return sp
+
+
+#***********************************************************************************************************************
+
+def addMultiTag(name, type, positions, blk, refs, metadata=None, extents=None):
+    '''
+    Add a multi_tag to one or more data_arrays
+    :param name: string, name of the multi_tag
+    :param type: string, type of the multi_tag
+    :param positions: quantities.Quantity, positions of the multi_tag
+    :param blk: nix.Block, the block in which the multi_tag is to be created
+    :param refs: list, list of nix.data_array objects, to which the multi_tag refers
+    :param metadata: nix.Section, to which the the multi_tag refers
+    :param extents: nix.data_array, extents of the multi_tag
+    :return: nix.multi_tag, the newly created multi_tag
+    '''
+
+    refUnits0 = refs[0].dimensions[0].unit
+    for ref in refs:
+        assert len(ref.dimensions) == 1, 'Only 1D refs are supported for now.'
+        assert ref.dimensions[0].unit == refUnits0, 'refs must have same time units'
+
+    positionsUnitsNormed = simpleFloat(positions / qu.Quantity(1, units=refUnits0))
+    positionsDA = createPosDA('{}_DA'.format(name), positionsUnitsNormed, blk)
+    tag = blk.create_multi_tag(name, type, positionsDA)
+    tag.units = [str(refUnits0)]
+
+    if extents is not None:
+        tag.extents = extents
+
+    for ref in refs:
+        tag.references.append(ref)
+
+    if metadata is not None:
+        tag.metadata = metadata
+
+#***********************************************************************************************************************
+
+def addTag(name, type, position, blk, refs, metadata=None, extent=None):
+    '''
+    Add a tag to one or more data_arrays
+    :param name: string, name of the tag
+    :param type: string, type of the tag
+    :param position: float, position of the tag
+    :param blk: nix.Block, the block in which the multi_tag is to be created
+    :param refs: list, list of nix.data_array objects, to which the multi_tag refers
+    :param metadata: nix.Section, to which the the multi_tag refers
+    :param extent: float, extent of the multi_tag
+    :return: nix.tag, the newly created tag
+    '''
+    tag = blk.create_tag(name, type, [position])
+
+
+
+    if extent is not None:
+        tag.extent = [extent]
+
+    for ref in refs:
+        tag.references.append(ref)
+        tag.units = [str(ref.dimensions[0].unit)]
+
+    if metadata is not None:
+        tag.metadata = metadata
+
+
+#***********************************************************************************************************************
+
+def simpleFloat(quant):
+    '''
+    Float or List of float(s) of simplified version of a quantity that can be
+    effectively represented as a float or list of floats.
+    :param quant: a quantity.Quantity or an iterable of quantity.Quantity objects
+    :return: float or iterable of floats depending on the argument quant
+    '''
+
+    # one element quantity
+    if quant.shape == ():
+
+        return float(quant.simplified)
+
+    # 1D quantity
+    elif len(quant.shape) == 1:
+
+        if quant.shape[0]:
+
+            return quant.simplified.magnitude.tolist()
+
+        else:
+
+            return []
+
+    # 2D quantity
+    elif len(quant.shape) == 2:
+
+        if quant.shape[0]:
+
+            # 2D column quantity
+            if quant.shape[1] == 1:
+                return quant.simplified.magnitude[:, 0].tolist()
+
+            # 2D row quantity
+            if quant.shape[0] == 1:
+                return quant.simplified.magnitude[0, :].tolist()
+
+            else:
+                raise (TypeError('simpleFloat only supports scalar, '
+                                 '1D, 2D row and 2D column quantities'))
+        else:
+            return []
+
+    else:
+
+        raise(TypeError('simpleFloat only supports scalar, '
+                        '1D, 2D row and 2D column quantities'))
+
+#***********************************************************************************************************************

paramLists/AdExpPars.py

@@ -0,0 +1,309 @@
+# Most of these parameters are part of the work by Aynur Makhsutov
+# during AMGEN program 2016 at Wachtlerlab, LMU. The last few parameters sets
+# that start with "DLInt" have been newly added.
+
+
+from brian2 import nA, mV, ms, nS, pF, nF, uA, uF
+
+
+resonator = {
+    "b": 0.0805 * nA,
+    "V":-70.4*mV,
+    "sF": 2 * mV,
+    "tau": 144 * ms,
+    "EL": -70.6 * mV,
+    "gL": 20 * nS,
+    "C": 2810 * pF,
+    "a": 8 * nS
+}
+
+integrator = {
+    "b": 0.0805 * nA,
+    "V":-70.4*mV,
+    "sF": 2 * mV,
+    "tau": 144 * ms,
+    "EL": -70.6 * mV,
+    "gL": 20 * nS,
+    "C": 12 * nF,
+    "a": 4 * nS
+}
+
+rebound = {
+    "w": 0 * uA,
+    "Vr": -60 * mV,  # -48.5*mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 720 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 281 * pF,
+    "a": 80 * nS
+}
+
+bursting = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -70.4 * mV,
+    "sF": 2 * mV,
+    "tau": 144 * ms,
+    "EL": -70.6 * mV,
+    "gL": 30 * nS,
+    "C": 281 * pF,
+    "a": 4 * nS
+}
+
+bursting_rebound = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 720 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 281 * pF,
+    "a": 80 * nS
+}
+
+bursting_rebound_low = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 720 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 281 * pF,
+    "a": 30 * nS
+}
+
+bursting_rebound_high = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 720 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 281 * pF,
+    "a": 150 * nS
+}
+
+
+perc = {
+    "Vr": 0.1,
+    "Vt": 0.1,
+    "b": 0.99,
+    "sF": 0.2,
+    "tau": 0.6,
+    "gL": 0.01,
+    "C": 0.2,
+    "a": 0.4
+}
+
+hopf_resonator = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 120 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 681 * pF,
+    "a": 80 * nS
+}
+
+hopf_resonator2 = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 120 * ms,
+    "EL": -60 * mV,
+    "gL": 6 * nS,
+    "C": 1200 * pF,
+    "a": 80 * nS
+}
+
+saddle_resonator = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 12 * ms,
+    "EL": -60 * mV,
+    "gL": 30 * nS,
+    "C": 800 * pF,
+    "a": 30 * nS
+}
+
+saddle_resonator2 = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 12 * ms,
+    "EL": -60 * mV,
+    "gL": 3 * nS,
+    "C": 800 * pF,
+    "a": 30 * nS
+}
+
+saddle_integrator = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 12 * ms,
+    "EL": -60 * mV,
+    "gL": 40 * nS,
+    "C": 1200 * pF,
+    "a": 3 * nS
+}
+
+saddle_mixed = {
+    "w": 0 * uA,
+    "Vr": -47.4 * mV,
+    "Vt": -50.4 * mV,
+    "b": 0.0805 * nA,
+    "V": -60 * mV,
+    "sF": 2 * mV,
+    "tau": 12 * ms,
+    "EL": -60 * mV,
+    "gL": 80 * nS,
+    "C": 300 * pF,
+    "a": 10 * nS
+}
+
+result13 = {
+    "w": 0 * uA,
+    "Vr": -62.8 * mV,
+    "Vt": -49.2 * mV,
+    "b": 1.41 * nA,
+    "V": -60 * mV,
+    "sF": 7.2 * mV,
+    "tau": 600 * ms,
+    "EL": -60 * mV,
+    "gL": 80 * nS,
+    "C": 276 * pF,
+    "a": 1037 * nS
+}
+
+mean_24_08_2016 = {
+    "w": 0 * uA,
+    "Vr": -55.56 * mV,
+    "Vt": -49.82 * mV,
+    "b": 1.85 * nA,
+    "V": -60 * mV,
+    "sF": 6.69 * mV,
+    "tau": 617.8 * ms,
+    "EL": -60 * mV,
+    "gL": 411.7 * nS,
+    "C": 5242.8 * pF,
+    "a": 923 * nS
+}
+
+std_inits = {
+            "w": 0*uA,
+            "Vr": -70.6 * mV,#-48.5*mV,
+            "Vt": -50.4 * mV,
+            "b": 0.0805 * nA,
+            "V":-70.4 * mV,
+            "sF": 2 * mV,
+            "tau": 144 * ms,
+            "EL": -70.6 * mV,
+            "gL": 30 * nS,
+            "C": 281 * pF,
+            "a": 4 * nS,
+            "Vp": -25 * mV,
+    "I": 0 * nA
+}
+
+DLInt2Try1 = {
+    "b": 0.0805 * nA,
+    "V": -30*mV,
+    "sF": 2 * mV,
+    "tau": 5 * ms,
+    "EL": -30 * mV,
+    "gL": 200000 * nS,
+    "C": 0.5 * uF,
+    "a": 4 * nS,
+    "Vr": -30 * mV,
+    "Vt": -20 * mV,
+    "Vp": -10 * mV
+}
+
+DLInt1Try1 = {
+    "b": 0.0805 * nA,
+    "V": -30*mV,
+    "sF": 2 * mV,
+    "tau": 1 * ms,
+    "EL": -30 * mV,
+    "gL": 200000 * nS,
+    "C": 0.5 * uF,
+    "a": 200000 * nS,
+    "Vr": -30 * mV,
+    "Vt": -15 * mV,
+    "Vp": -5 * mV
+}
+
+DLInt1Try2 = {
+    "b": 1.367 * nA,
+    "V": -30*mV,
+    "sF": 6 * mV,
+    "tau": 100 * ms,
+    "EL": -30 * mV,
+    "gL": 200000 * nS,
+    "C": 0.5 * uF,
+    "a": 400000 * nS,
+    "Vr": -30 * mV,
+    "Vt": -25 * mV,
+    "Vp": 0 * mV
+}
+
+DLInt1Aynur = {
+    "b": 1 * nA,
+    "V": -30 * mV,
+    "sF": 6 * mV,
+    "tau": 180 * ms,
+    "EL": -30 * mV,
+    "gL": 500 * nS,
+    "C": 0.125 * nF,
+    "a": 500 * nS,
+    "Vr": -31 * mV,
+    "Vt": -27.5 * mV,
+    "Vp": 0 * mV
+}
+
+DLInt2Try2 = {
+    "b": 1 * nA,
+    "V": -30 * mV,
+    "sF": 6 * mV,
+    "tau": 0.08 * ms,
+    "EL": -30 * mV,
+    "gL": 500 * nS,
+    "C": 0.125 * nF,
+    "a": 500 * nS,
+    "Vr": -31 * mV,
+    "Vt": -27.5 * mV,
+    "Vp": 0 * mV
+}

paramLists/inputParsList.py

@@ -0,0 +1,79 @@
+from brian2 import units, array
+from brian2.units.fundamentalunits import Quantity
+import numpy as np
+
+period265 = (1 / 265)
+
+onePulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([0.9 * period265]) * units.second)
+twoPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([1.9 * period265]) * units.second)
+threePulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([2.9 * period265]) * units.second)
+
+tenMSPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([10]) * units.ms)
+twentyMSPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([20]) * units.ms)
+thirtyMSPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([30]) * units.ms)
+fortyMSPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([40]) * units.ms)
+fiftyMSPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([50]) * units.ms)
+
+
+
+oneSecondPulse = dict(sinPulseStarts=array([0]) * units.ms,
+                sinPulseDurs=array([1]) * units.second)
+
+def getPulseTrainInputPars(pulseDur: Quantity, pulseInt: Quantity,
+                           stimDur: Quantity) -> dict:
+    pulseDurF = float(pulseDur)
+    pulseIntF = float(pulseInt)
+    stimDurF = float(stimDur)
+
+    sinPulseStarts = (np.arange(0, stimDurF, pulseIntF)) * units.second
+    sinPulseDurs = ([pulseDurF] * len(sinPulseStarts)) * units.second
+
+    return dict(sinPulseStarts=sinPulseStarts,
+                sinPulseDurs=sinPulseDurs)
+
+pulseTrainInt20Dur10 = getPulseTrainInputPars(pulseDur=10*units.ms, pulseInt=20*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt20Dur16 = getPulseTrainInputPars(pulseDur=16*units.ms, pulseInt=20*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt33Dur10 = getPulseTrainInputPars(pulseDur=10*units.ms, pulseInt=33*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt33Dur16 = getPulseTrainInputPars(pulseDur=16*units.ms, pulseInt=33*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt50Dur10 = getPulseTrainInputPars(pulseDur=10*units.ms, pulseInt=50*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt50Dur16 = getPulseTrainInputPars(pulseDur=16*units.ms, pulseInt=50*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt50Dur20 = getPulseTrainInputPars(pulseDur=20*units.ms, pulseInt=50*units.ms,
+                                              stimDur=1*units.second)
+pulseTrainInt50Dur30 = getPulseTrainInputPars(pulseDur=30*units.ms, pulseInt=50*units.ms,
+                                              stimDur=1*units.second)
+pTShortInt20Dur10 = getPulseTrainInputPars(pulseDur=10 * units.ms, pulseInt=20 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt20Dur16 = getPulseTrainInputPars(pulseDur=16 * units.ms, pulseInt=20 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt33Dur10 = getPulseTrainInputPars(pulseDur=10 * units.ms, pulseInt=33 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt33Dur16 = getPulseTrainInputPars(pulseDur=16 * units.ms, pulseInt=33 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt33Dur20 = getPulseTrainInputPars(pulseDur=20 * units.ms, pulseInt=33 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt50Dur10 = getPulseTrainInputPars(pulseDur=10 * units.ms, pulseInt=50 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt50Dur16 = getPulseTrainInputPars(pulseDur=16 * units.ms, pulseInt=50 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt50Dur20 = getPulseTrainInputPars(pulseDur=20 * units.ms, pulseInt=50 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt100Dur10 = getPulseTrainInputPars(pulseDur=10 * units.ms, pulseInt=100 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt100Dur16 = getPulseTrainInputPars(pulseDur=16 * units.ms, pulseInt=100 * units.ms,
+                                           stimDur=250 * units.ms)
+pTShortInt100Dur20 = getPulseTrainInputPars(pulseDur=20 * units.ms, pulseInt=100 * units.ms,
+                                           stimDur=250 * units.ms)

paramLists/synapsePropsList.py

@@ -0,0 +1,31 @@
+from brian2 import units
+
+DLInt1_syn_try1 = dict(wSyn=[5000, 400] * units.usiemens,
+                    Esyn=[0, -80] * units.mvolt,
+                    tau1=[4, 4] * units.ms, tau2=[4.5, 8] * units.ms,
+                    delay=[6, 10] * units.ms)
+
+DLInt1_syn_try2 = dict(wSyn=[15000, 1500] * units.nsiemens,
+                    Esyn=[0, -80] * units.mvolt,
+                    tau1=[4, 4] * units.ms, tau2=[4.5, 8] * units.ms,
+                    delay=[6, 18] * units.ms)
+
+DLInt1_syn_try2_e = dict(wSyn=25 * units.nsiemens,
+                    Esyn=0 * units.mvolt,
+                    tau1=0.5 * units.ms, tau2=2 * units.ms,
+                    delay=5 * units.ms)
+
+DLInt1_syn_try2_i = dict(wSyn=160 * units.nsiemens,
+                    Esyn=-80 * units.mvolt,
+                    tau1=12 * units.ms, tau2=15 * units.ms,
+                    delay=10 * units.ms)
+
+DLInt2_syn_try2 = dict(wSyn=75 * units.nsiemens,
+                    Esyn=0 * units.mvolt,
+                    tau1=0.5 * units.ms, tau2=2 * units.ms,
+                    delay=5 * units.ms)
+
+DLInt1_DLInt2_try1 = dict(wSyn=500 * units.nsiemens,
+                    Esyn=-80 * units.mvolt,
+                    tau1=7 * units.ms, tau2=8 * units.ms,
+                    delay=3 * units.ms)

plotDLInt1DLInt2SynEffect.py

@@ -0,0 +1,182 @@
+import nixio
+from matplotlib import pyplot as plt
+from brian2 import units
+from dirDefs import homeFolder
+import os
+from neoNIXIO import dataArray2AnalogSignal, multiTag2SpikeTrain, simpleFloat
+from mplPars import mplPars
+import seaborn as sns
+import quantities as qu
+
+sns.set(rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 450 * units.ms
+totalSimDur = simSettleTime + simDuration
+IntDurs = [
+    (20, 10),
+    (20, 16),
+    (33, 10),
+    (33, 16),
+    (33, 20),
+    (50, 10),
+    (50, 16),
+    (50, 20),
+    (100, 10),
+    (100, 16),
+    (100, 20)
+]
+
+pulseInts = sorted(set([x[0] for x in IntDurs]))
+pulseDurs = sorted(set([x[1] for x in IntDurs]))
+
+showBefore = 75 * units.ms
+showAfter = -30 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+
+DLInt2ModelProps = "DLInt2Try2"
+
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+# DLInt2SynapseProps = ""
+
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+# DLInt1DLInt2SynProps = ""
+
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                     DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps)
+
+fig1, axs1 = plt.subplots(nrows=len(pulseDurs), ncols=len(pulseInts),
+                          figsize=(14, 11.2), sharex='col')
+fig2, axs2 = plt.subplots(nrows=len(pulseDurs), ncols=len(pulseInts),
+                          figsize=(14, 11.2), sharex='col')
+
+for IntDur in IntDurs:
+
+    pulseInt = IntDur[0]
+    pulseDur = IntDur[1]
+    inputParsName = 'pTShortInt{:2d}Dur{:2d}'.format(pulseInt, pulseDur)
+    opDirWith = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+    OPNixFileWith = os.path.join(opDirWith, 'SimResults.h5')
+
+    opDirWithout = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                             DLInt1SynapseProps + DLInt2SynapseProps,
+                             inputParsName)
+    OPNixFileWithout = os.path.join(opDirWithout, 'SimResults.h5')
+
+    nixFile = nixio.File.open(OPNixFileWith, nixio.FileMode.ReadOnly)
+    blk = nixFile.blocks["Simulation Traces"]
+    dlint1MemV = blk.data_arrays["DLInt1 MemV"]
+    dlint1SpikesMT = blk.multi_tags["DLInt1 Spikes"]
+    dlint2MemV = blk.data_arrays["DLInt2 MemV"]
+    dlint2SpikesMT = blk.multi_tags["DLInt2 Spikes"]
+    sinInput = blk.data_arrays["Input Vibration Signal"]
+    joSpikesMT = blk.multi_tags["JO Spikes"]
+
+    dlint1MemVAS = dataArray2AnalogSignal(dlint1MemV)
+    dlint2MemVAS = dataArray2AnalogSignal(dlint2MemV)
+    sinInputAS = dataArray2AnalogSignal(sinInput)
+    dlint1SpikesST = multiTag2SpikeTrain(dlint1SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+    dlint2SpikesST = multiTag2SpikeTrain(dlint2SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+    joSpikesST = multiTag2SpikeTrain(joSpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+
+    nixFileWithout = nixio.File.open(OPNixFileWithout, nixio.FileMode.ReadOnly)
+    blkWithout = nixFileWithout.blocks["Simulation Traces"]
+    dlint2MemVWithout = blkWithout.data_arrays["DLInt2 MemV"]
+    dlint2SpikesMTWithout = blkWithout.multi_tags["DLInt2 Spikes"]
+    dlint2MemVASWithout = dataArray2AnalogSignal(dlint2MemVWithout)
+    dlint2SpikesSTWithout = multiTag2SpikeTrain(dlint2SpikesMTWithout,
+                                                sinInputAS.t_start, sinInputAS.t_stop)
+
+
+    rowInd = pulseDurs.index(pulseDur)
+    colInd = pulseInts.index(pulseInt)
+
+    axs1[rowInd, colInd].plot(simpleFloat(dlint1MemVAS.times / qu.ms),
+                              simpleFloat(dlint1MemVAS / qu.mV), 'b-', lw=1)
+    # mew needs setting for seaborn. https://github.com/mwaskom/seaborn/issues/644
+    axs1[rowInd, colInd].plot(simpleFloat(dlint1SpikesST.times / qu.ms),
+                              [4] * dlint1SpikesST.shape[0],
+                              'b|', ms=8, mew=1)
+    axs1[rowInd, colInd].plot(simpleFloat(sinInputAS.times / qu.ms),
+                              simpleFloat(-50 + (sinInputAS * 5) / qu.um)
+                              , 'k-', lw=1)
+    axs1[rowInd, colInd].set_xlim([(simSettleTime - showBefore) / units.ms,
+                                   (totalSimDur + showAfter) / units.ms])
+
+
+    axs2[rowInd, colInd].plot(simpleFloat(dlint2MemVAS.times / qu.ms),
+                              simpleFloat(dlint2MemVAS / qu.mV), 'b-', lw=1)
+    axs2[rowInd, colInd].plot(simpleFloat(dlint2MemVASWithout.times / qu.ms),
+                              simpleFloat(-45 + (dlint2MemVASWithout / qu.mV)),
+                              'r-', lw=1)
+    axs2[rowInd, colInd].plot(simpleFloat(dlint2SpikesST.times / qu.ms),
+                              [12] * dlint2SpikesST.shape[0],
+                            'b|', ms=8, mew=1)
+    axs2[rowInd, colInd].plot(simpleFloat(dlint2SpikesSTWithout.times / qu.ms),
+                              [6] * dlint2SpikesSTWithout.shape[0],
+                              'r|', ms=8, mew=1)
+    axs2[rowInd, colInd].plot(simpleFloat(sinInputAS.times / qu.ms),
+                              simpleFloat(-105 + (sinInputAS * 7.5) / qu.um)
+                              , 'k-', lw=1)
+    axs2[rowInd, colInd].set_xlim([(simSettleTime - showBefore) / units.ms,
+                                   (totalSimDur + showAfter) / units.ms])
+
+
+for rowInd in range(axs1.shape[0]):
+    for colInd in range(axs1.shape[1]):
+        ax = axs1[rowInd, colInd]
+        ax.set_ylim([-60, 10])
+        ax.yaxis.tick_right()
+        ax.set_yticklabels([""] * len(ax.get_yticks()))
+        ax.set_xticklabels([""] * len(ax.get_xticks()))
+
+for rowInd in range(axs2.shape[0]):
+    for colInd in range(axs2.shape[1]):
+        ax = axs2[rowInd, colInd]
+        ax.set_ylim([-120, 20])
+        ax.yaxis.tick_right()
+        ax.set_yticklabels([""] * len(ax.get_yticks()))
+        ax.set_xticklabels([""] * len(ax.get_xticks()))
+
+for ind, val in enumerate(pulseInts):
+
+    axs1[0, ind].set_title(str(val))
+    axs2[0, ind].set_title(str(val))
+
+for ind, val in enumerate(pulseDurs):
+
+    axs1[ind, 0].set_ylabel(str(val))
+    axs2[ind, 0].set_ylabel(str(val))
+
+fig1.tight_layout()
+fig2.tight_layout()
+
+fig1.savefig(os.path.join(opDir, 'DLInt1Summary.png'), dpi=150)
+fig2.savefig(os.path.join(opDir, 'DLInt2Summary.png'), dpi=150)
+
+
+
+
+
+
+
+
+

plotMemVs.py

@@ -0,0 +1,125 @@
+import nixio
+from dirDefs import homeFolder
+import os
+import seaborn as sns
+from mplPars import mplPars
+from brian2 import units
+from matplotlib import pyplot as plt
+from neoNIXIO import multiTag2SpikeTrain, dataArray2AnalogSignal, simpleFloat
+import quantities as qu
+
+mplPars["axes.titlesize"] = 14
+mplPars["font.size"] = 14
+mplPars["xtick.labelsize"] = 12
+mplPars["ytick.labelsize"] = 12
+mplPars["legend.fontsize"] = 12
+sns.set(rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 150 * units.ms
+# # inputParsName = 'onePulse'
+# # inputParsName = 'twoPulse'
+# # inputParsName = 'threePulse'
+# inputParsName = "thirtyMSPulse"
+# # inputParsName = "fortyMSPulse"
+#
+# showBefore = 50 * units.ms
+# showAfter = 0 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 450 * units.ms
+# # inputParsName = "pTShortInt20Dur10"
+# # inputParsName = "pTShortInt20Dur16"
+# # inputParsName = "pTShortInt33Dur10"
+# # inputParsName = "pTShortInt33Dur16"
+# # inputParsName = "pTShortInt33Dur20"
+# # inputParsName = "pTShortInt50Dur10"
+# # inputParsName = "pTShortInt50Dur16"
+# # inputParsName = "pTShortInt50Dur20"
+# inputParsName = "pTShortInt100Dur10"
+# # inputParsName = "pTShortInt100Dur16"
+# # inputParsName = "pTShortInt100Dur20"
+#
+# showBefore = 100 * units.ms
+# showAfter = 100 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 1500 * units.ms
+inputParsName = 'oneSecondPulse'
+# inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# inputParsName = 'pulseTrainInt33Dur10'
+# inputParsName = 'pulseTrainInt33Dur16'
+
+showBefore = 500 * units.ms
+showAfter = 0 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+DLInt2ModelProps = "DLInt2Try2"
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+OPNixFile = os.path.join(opDir, 'SimResults.h5')
+
+
+totalSimDur = simDuration + simSettleTime
+
+nixFile = nixio.File.open(OPNixFile, nixio.FileMode.ReadOnly)
+blk = nixFile.blocks["Simulation Traces"]
+dlint1MemV = blk.data_arrays["DLInt1 MemV"]
+dlint1SpikesMT = blk.multi_tags["DLInt1 Spikes"]
+dlint2MemV = blk.data_arrays["DLInt2 MemV"]
+dlint2SpikesMT = blk.multi_tags["DLInt2 Spikes"]
+sinInput = blk.data_arrays["Input Vibration Signal"]
+joSpikesMT = blk.multi_tags["JO Spikes"]
+
+dlint1MemVAS = dataArray2AnalogSignal(dlint1MemV)
+dlint2MemVAS = dataArray2AnalogSignal(dlint2MemV)
+sinInputAS = dataArray2AnalogSignal(sinInput)
+dlint1SpikesST = multiTag2SpikeTrain(dlint1SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+dlint2SpikesST = multiTag2SpikeTrain(dlint2SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+joSpikesST = multiTag2SpikeTrain(joSpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+
+fig1, axs1 = plt.subplots(nrows=2, figsize=(7, 4.375), sharex='col')
+axs1[0].plot(simpleFloat(dlint1MemVAS.times / qu.ms),
+                simpleFloat(dlint1MemVAS / qu.mV), 'b-', lw=0.5)
+axs1[0].plot(simpleFloat(sinInputAS.times / qu.ms),
+                simpleFloat((sinInputAS * 2.5 - 55 * qu.um) / qu.um),
+               'k-', lw=0.5)
+axs1[0].set_ylabel("DL-Int-1")
+axs1[0].set_ylim([-60, 10])
+
+axs1[1].plot(simpleFloat(dlint2MemVAS.times / qu.ms),
+                simpleFloat(dlint2MemVAS / qu.mV), 'b-', lw=0.5)
+
+axs1[1].plot(simpleFloat(sinInputAS.times / qu.ms),
+                simpleFloat((sinInputAS * 2.5 - 55 * qu.um) / qu.um),
+               'k-', lw=0.5)
+axs1[1].set_ylabel("DL-Int-2")
+# axs1[1].set_xlabel("Time (ms)")
+
+axs1[1].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+axs1[0].set_yticklabels([""] * len(axs1[1].get_yticks()))
+axs1[1].set_yticklabels([""] * len(axs1[1].get_yticks()))
+axs1[1].set_xticklabels([""] * len(axs1[1].get_xticks()))
+
+for fig in [fig1]:
+    fig.tight_layout()
+
+fig1.savefig(os.path.join(opDir, "Traces.png"), dpi=300)

plotShortStims.py

@@ -0,0 +1,126 @@
+from brian2 import units
+from matplotlib import pyplot as plt
+import seaborn as sns
+import os
+from dirDefs import homeFolder
+import nixio
+from neoNIXIO import multiTag2SpikeTrain, dataArray2AnalogSignal, simpleFloat
+import quantities as qu
+from mplPars import mplPars
+
+sns.set(rc=mplPars)
+sns.axes_style('whitegrid')
+
+simSettleTime = 600 * units.ms
+#
+simStepSize = 0.1 * units.ms
+simDuration = 150 * units.ms
+inputParsNames = {
+                     10: "tenMSPulse",
+                     20: "twentyMSPulse",
+                     30: "thirtyMSPulse",
+                     40: "fortyMSPulse"
+                     }
+showBefore = 50 * units.ms
+showAfter = 50 * units.ms
+
+totalSimDur = simSettleTime + simDuration
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+
+DLInt2ModelProps = "DLInt2Try2"
+
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+# DLInt2SynapseProps = ""
+
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+# DLInt1DLInt2SynProps = ""
+
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                     DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps)
+
+fig, axs = plt.subplots(nrows=2, ncols=len(inputParsNames), figsize=(14, 11.2),
+                        sharex='col')
+
+for ipInd, (ipVal, ipName) in enumerate(inputParsNames.items()):
+
+    nixFile = os.path.join(opDir, ipName, 'SimResults.h5')
+    nixFile = nixio.File.open(nixFile, nixio.FileMode.ReadOnly)
+    blk = nixFile.blocks["Simulation Traces"]
+    dlint1MemV = blk.data_arrays["DLInt1 MemV"]
+    dlint1SpikesMT = blk.multi_tags["DLInt1 Spikes"]
+    dlint2MemV = blk.data_arrays["DLInt2 MemV"]
+    dlint2SpikesMT = blk.multi_tags["DLInt2 Spikes"]
+    sinInput = blk.data_arrays["Input Vibration Signal"]
+    joSpikesMT = blk.multi_tags["JO Spikes"]
+
+    dlint1MemVAS = dataArray2AnalogSignal(dlint1MemV)
+    dlint2MemVAS = dataArray2AnalogSignal(dlint2MemV)
+    sinInputAS = dataArray2AnalogSignal(sinInput)
+    dlint1SpikesST = multiTag2SpikeTrain(dlint1SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+    dlint2SpikesST = multiTag2SpikeTrain(dlint2SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+    joSpikesST = multiTag2SpikeTrain(joSpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+
+    axs[0, ipInd].plot(simpleFloat(dlint1MemVAS.times / qu.ms),
+                              simpleFloat(dlint1MemVAS / qu.mV), 'b-')
+    # mew needs setting for seaborn. https://github.com/mwaskom/seaborn/issues/644
+    axs[0, ipInd].plot(simpleFloat(dlint1SpikesST.times / qu.ms),
+                              [4] * dlint1SpikesST.shape[0],
+                              'b|', ms=8, mew=1)
+    markerline, stemlines, baseline \
+        = axs[0, ipInd].stem(simpleFloat(joSpikesST.times / qu.ms),
+                      [-40] * joSpikesST.shape[0],
+                      linefmt='r-.', markerfmt='None', basefmt='None',
+                      bottom=-60)
+    plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+    axs[0, ipInd].plot(simpleFloat(sinInputAS.times / qu.ms),
+                              simpleFloat(((5 * qu.um * sinInputAS) - 50 * qu.um) / qu.um)
+                              , 'k-')
+    # axs[0, ipInd].set_xlim([(simSettleTime - showBefore) / units.ms,
+    #                                (totalSimDur + showAfter) / units.ms])
+    axs[0, ipInd].set_ylim([-60, 10])
+    axs[0, ipInd].yaxis.tick_right()
+
+
+    axs[1, ipInd].plot(simpleFloat(dlint2MemVAS.times / qu.ms),
+                       simpleFloat(dlint2MemVAS / qu.mV), 'b-')
+    # mew needs setting for seaborn. https://github.com/mwaskom/seaborn/issues/644
+    axs[1, ipInd].plot(simpleFloat(dlint2SpikesST.times / qu.ms),
+                       [4] * dlint2SpikesST.shape[0],
+                       'b|', ms=8, mew=1)
+    markerline, stemlines, baseline \
+        = axs[1, ipInd].stem(simpleFloat(joSpikesST.times / qu.ms),
+                             [-40] * joSpikesST.shape[0],
+                             linefmt='r-.', markerfmt='None', basefmt='None',
+                             bottom=-60)
+    plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+    axs[1, ipInd].plot(simpleFloat(sinInputAS.times / qu.ms),
+                       simpleFloat(((5 * qu.um * sinInputAS) - 50 * qu.um) / qu.um)
+                       , 'k-')
+    axs[1, ipInd].set_xlim([(simSettleTime - showBefore) / units.ms,
+                            (totalSimDur + showAfter) / units.ms])
+    axs[1, ipInd].set_ylim([-60, 10])
+    axs[1, ipInd].yaxis.tick_right()
+
+for ind in range(len(inputParsNames) - 1):
+    axs[0, ind].set_yticks([])
+    axs[1, ind].set_yticks([])
+
+for ipInd, (ipVal, ipName) in enumerate(inputParsNames.items()):
+    axs[0, ipInd].set_title(ipVal)
+
+axs[0, 0].set_ylabel("DL-Int-1")
+axs[1, 0].set_ylabel("DL-Int-2")
+
+fig.tight_layout()
+fig.savefig(os.path.join(opDir, "shortStims.png"), dpi=300)

plotSynCurrents.py

@@ -0,0 +1,203 @@
+import nixio
+from dirDefs import homeFolder
+import os
+import seaborn as sns
+from mplPars import mplPars
+from brian2 import units
+from matplotlib import pyplot as plt
+from neoNIXIO import multiTag2SpikeTrain, dataArray2AnalogSignal, simpleFloat
+import quantities as qu
+
+sns.set(rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 150 * units.ms
+# inputParsName = 'onePulse'
+# inputParsName = 'twoPulse'
+# inputParsName = 'threePulse'
+inputParsName = "thirtyMSPulse"
+# inputParsName = "fortyMSPulse"
+
+showBefore = 50 * units.ms
+showAfter = 0 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 450 * units.ms
+# # inputParsName = "pTShortInt20Dur10"
+# # inputParsName = "pTShortInt20Dur16"
+# # inputParsName = "pTShortInt33Dur10"
+# # inputParsName = "pTShortInt33Dur16"
+# # inputParsName = "pTShortInt33Dur20"
+# # inputParsName = "pTShortInt50Dur10"
+# # inputParsName = "pTShortInt50Dur16"
+# # inputParsName = "pTShortInt50Dur20"
+# inputParsName = "pTShortInt100Dur10"
+# # inputParsName = "pTShortInt100Dur16"
+# # inputParsName = "pTShortInt100Dur20"
+#
+# showBefore = 100 * units.ms
+# showAfter = 100 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 1500 * units.ms
+# # inputParsName = 'oneSecondPulse'
+# # inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# # inputParsName = 'pulseTrainInt33Dur10'
+# # inputParsName = 'pulseTrainInt33Dur16'
+# showBefore = 500 * units.ms
+# showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+DLInt2ModelProps = "DLInt2Try2"
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+OPNixFile = os.path.join(opDir, 'simResWithSynCurrents.h5')
+
+
+totalSimDur = simDuration + simSettleTime
+
+nixFile = nixio.File.open(OPNixFile, nixio.FileMode.ReadOnly)
+blk = nixFile.blocks["Simulation Traces"]
+dlint1MemV = blk.data_arrays["DLInt1 MemV"]
+isynEDLInt1 = blk.data_arrays["DL-Int-1 input EPSC"]
+isynIDLInt1 = blk.data_arrays["DL-Int-1 input IPSC"]
+dlint1SpikesMT = blk.multi_tags["DLInt1 Spikes"]
+dlint2MemV = blk.data_arrays["DLInt2 MemV"]
+isynEDLInt2 = blk.data_arrays["DL-Int-2 input EPSC"]
+isynIDLInt2 = blk.data_arrays["DL-Int-2 input IPSC"]
+dlint2SpikesMT = blk.multi_tags["DLInt2 Spikes"]
+sinInput = blk.data_arrays["Input Vibration Signal"]
+joSpikesMT = blk.multi_tags["JO Spikes"]
+
+dlint1MemVAS = dataArray2AnalogSignal(dlint1MemV)
+isynEASDLInt1 = dataArray2AnalogSignal(isynEDLInt1)
+isynIASDLInt1 = dataArray2AnalogSignal(isynIDLInt1)
+dlint2MemVAS = dataArray2AnalogSignal(dlint2MemV)
+isynEASDLInt2 = dataArray2AnalogSignal(isynEDLInt2)
+isynIASDLInt2 = dataArray2AnalogSignal(isynIDLInt2)
+sinInputAS = dataArray2AnalogSignal(sinInput)
+dlint1SpikesST = multiTag2SpikeTrain(dlint1SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+dlint2SpikesST = multiTag2SpikeTrain(dlint2SpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+joSpikesST = multiTag2SpikeTrain(joSpikesMT, sinInputAS.t_start, sinInputAS.t_stop)
+
+fig1, ax1 = plt.subplots(nrows=2, ncols=2, figsize=(14, 11.2), sharex='col')
+ax1[0, 0].plot(simpleFloat(dlint1MemVAS.times / qu.ms),
+            simpleFloat(dlint1MemVAS / qu.mV), 'b-')
+markerline, stemlines, baseline \
+    = ax1[0, 0].stem(simpleFloat(joSpikesST.times / qu.ms),
+                     [dlint1MemVAS.magnitude.min()] * joSpikesST.shape[0],
+                     linefmt='r-.', markerfmt='None', basefmt='None',
+                     bottom=-52.5)
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+markerline, stemlines, baseline \
+    = ax1[0, 0].stem(simpleFloat(joSpikesST.times / qu.ms),
+                     [10] * joSpikesST.shape[0],
+                     linefmt='r-.', markerfmt='None', basefmt='None',
+                     bottom=dlint1MemVAS.magnitude.max())
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+ax1[0, 0].plot(simpleFloat(sinInputAS.times / qu.ms),
+               simpleFloat((sinInputAS * 2.5 - 55 * qu.um) / qu.um),
+               'k-')
+
+ax1[0, 1].plot(simpleFloat(isynEASDLInt1.times / qu.ms),
+               simpleFloat(isynEASDLInt1 / qu.nA), color=[0, 0.6, 0],
+               ls='-', marker='None')
+ax1[0, 1].plot(simpleFloat(isynIASDLInt1.times / qu.ms),
+            simpleFloat(isynIASDLInt1 / qu.nA), color=[1, 0, 0],
+            ls='-', marker='None')
+markerline, stemlines, baseline \
+    = ax1[0, 1].stem(simpleFloat(joSpikesST.times / qu.ms),
+                  [isynIASDLInt1.magnitude.min()] * joSpikesST.shape[0],
+                  linefmt='r-.', markerfmt='None', basefmt='None',
+                  bottom=-5.125)
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+markerline, stemlines, baseline \
+    = ax1[0, 1].stem(simpleFloat(joSpikesST.times / qu.ms),
+                     [5] * joSpikesST.shape[0],
+                     linefmt='r-.', markerfmt='None', basefmt='None',
+                     bottom=isynEASDLInt1.magnitude.max())
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+ax1[0, 1].plot(simpleFloat(sinInputAS.times / qu.ms),
+               simpleFloat((sinInputAS * 0.375 - 5.5 * qu.um) / qu.um),
+               'k-')
+
+ax1[1, 0].plot(simpleFloat(dlint2MemVAS.times / qu.ms),
+            simpleFloat(dlint2MemVAS / qu.mV), 'b-')
+markerline, stemlines, baseline \
+    = ax1[1, 0].stem(simpleFloat(joSpikesST.times / qu.ms),
+                  [dlint2MemVAS.magnitude.min()] * joSpikesST.shape[0],
+                  linefmt='r-.', markerfmt='None', basefmt='None',
+                  bottom=-52.5)
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+markerline, stemlines, baseline \
+    = ax1[1, 0].stem(simpleFloat(joSpikesST.times / qu.ms),
+                     [10] * joSpikesST.shape[0],
+                     linefmt='r-.', markerfmt='None', basefmt='None',
+                     bottom=dlint2MemVAS.magnitude.max())
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+ax1[1, 0].plot(simpleFloat(sinInputAS.times / qu.ms),
+               simpleFloat((sinInputAS * 2.5 - 55 * qu.um) / qu.um),
+               'k-')
+
+ax1[1, 1].plot(simpleFloat(isynEASDLInt2.times / qu.ms),
+            simpleFloat(isynEASDLInt2 / qu.nA), color=[0, 0.6, 0],
+            ls='-', marker='None')
+ax1[1, 1].plot(simpleFloat(isynIASDLInt2.times / qu.ms),
+            simpleFloat(isynIASDLInt2 / qu.nA), color=[1, 0, 0],
+            ls='-', marker='None')
+markerline, stemlines, baseline \
+    = ax1[1, 1].stem(simpleFloat(joSpikesST.times / qu.ms),
+                  [isynIASDLInt2.magnitude.min()] * joSpikesST.shape[0],
+                  linefmt='r-.', markerfmt='None', basefmt='None',
+                  bottom=-5.125)
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+markerline, stemlines, baseline \
+    = ax1[1, 1].stem(simpleFloat(joSpikesST.times / qu.ms),
+                  [5] * joSpikesST.shape[0],
+                  linefmt='r-.', markerfmt='None', basefmt='None',
+                  bottom=isynEASDLInt2.magnitude.max())
+plt.setp(stemlines, color=(0.5, 0.5, 0.5), lw=2)
+
+ax1[1, 1].plot(simpleFloat(sinInputAS.times / qu.ms),
+               simpleFloat((sinInputAS * 0.375 - 5.5 * qu.um) / qu.um),
+               'k-')
+
+ax1[1, 0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+
+ax1[1, 1].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+
+for ax in ax1.flat:
+    ax.set_xticklabels([""] * len(ax.get_xticks()))
+    ax.set_yticklabels([""] * len(ax.get_yticks()))
+
+
+for fig in [fig1]:
+    fig.tight_layout()
+
+fig1.savefig(os.path.join(opDir, "DLInt-1_2memVSynCurrents.png"), dpi=150)
+

runJODLInt1DLInt2Multiple.py

@@ -0,0 +1,78 @@
+from JODLInt1DLInt2 import runJODLInt1DLInt2
+from brian2 import units
+
+simSettleTime = 600 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 150 * units.ms
+# inputParsNames = [
+#     'onePulse',
+#     'twoPulse',
+#     'threePulse',
+#     "tenMSPulse",
+#     "twentyMSPulse",
+#     "thirtyMSPulse",
+#     "fortyMSPulse",
+# ]
+# showBefore = 50 * units.ms
+# showAfter = 50 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 450 * units.ms
+# inputParsNames = [
+#     "pTShortInt20Dur10",
+#     "pTShortInt20Dur16",
+#     "pTShortInt33Dur10",
+#     "pTShortInt33Dur16",
+#     "pTShortInt33Dur20",
+#     "pTShortInt50Dur10",
+#     "pTShortInt50Dur16",
+#     "pTShortInt50Dur20",
+#     "pTShortInt100Dur10",
+#     "pTShortInt100Dur16",
+#     "pTShortInt100Dur20",
+# ]
+# showBefore = 100 * units.ms
+# showAfter = 100 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 1500 * units.ms
+inputParsNames = [
+    'oneSecondPulse',
+    # 'pulseTrainInt33Dur10',
+    # 'pulseTrainInt33Dur16',
+    # 'pulseTrainInt20Dur10',
+    # 'pulseTrainInt20Dur16',
+    # 'pulseTrainInt50Dur10',
+    # 'pulseTrainInt50Dur16',
+    # 'pulseTrainInt50Dur20',
+]
+showBefore = 500 * units.ms
+showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+
+DLInt2ModelProps = "DLInt2Try2"
+
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+# DLInt2SynapseProps = ""
+
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+# DLInt1DLInt2SynProps = ""
+
+for inputParsName in inputParsNames:
+    runJODLInt1DLInt2(simStepSize, simDuration, simSettleTime,
+                      inputParsName, showBefore, showAfter,
+                      DLInt1ModelProps, DLInt2ModelProps,
+                      DLInt1SynapsePropsE, DLInt1SynapsePropsI,
+                      DLInt2SynapseProps, DLInt1DLInt2SynProps,
+                      askReplace=False
+                      )

setup.py

@@ -0,0 +1,16 @@
+from setuptools import setup, find_packages
+setup(
+        name="hb_pac_disNet",
+        use_scm_version=True,
+        setup_requires=['setuptools_scm'],
+        packages=find_packages(exclude=["^\."]),
+        exclude_package_data={'': ["Readme.md", "tests"]},
+        install_requires=["numpy>=1.11.2",
+                          "matplotlib>=1.5.3",
+                          "seaborn>=0.7.1",
+                          "neo>=0.5.0",
+                          "nixio>=1.3",
+                          "brian2>=2.0.1",
+                          "ipython>=6.1"],
+        python_requires=">=3.5",
+    )

simSynCurrents.py

@@ -0,0 +1,389 @@
+import os
+import sys
+
+import seaborn as sns
+from brian2 import defaultclock, units, StateMonitor
+from matplotlib import pyplot as plt
+from brian2.core.network import Network
+from dirDefs import homeFolder
+from models.neuronModels import VSNeuron, JOSpikes265, getSineInput
+from models.neurons import AdExp
+from models.synapses import exp2Syn, exp2SynStateInits
+from mplPars import mplPars
+from paramLists import synapsePropsList, inputParsList, AdExpPars
+
+
+from neo import AnalogSignal
+import nixio
+from neoNIXIO import addAnalogSignal2Block, addMultiTag
+import quantities as qu
+from brianUtils import addBrianQuantity2Section
+sns.set(style="whitegrid", rc=mplPars)
+
+
+simSettleTime = 600 * units.ms
+
+simStepSize = 0.1 * units.ms
+simDuration = 150 * units.ms
+# inputParsName = 'onePulse'
+# inputParsName = 'twoPulse'
+# inputParsName = 'threePulse'
+inputParsName = "thirtyMSPulse"
+# inputParsName = "fortyMSPulse"
+showBefore = 50 * units.ms
+showAfter = 50 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 450 * units.ms
+# # inputParsName = "pTShortInt20Dur10"
+# # inputParsName = "pTShortInt20Dur16"
+# # inputParsName = "pTShortInt33Dur10"
+# # inputParsName = "pTShortInt33Dur16"
+# # inputParsName = "pTShortInt33Dur20"
+# # inputParsName = "pTShortInt50Dur10"
+# # inputParsName = "pTShortInt50Dur16"
+# # inputParsName = "pTShortInt50Dur20"
+# inputParsName = "pTShortInt100Dur10"
+# # inputParsName = "pTShortInt100Dur16"
+# # inputParsName = "pTShortInt100Dur20"
+#
+# showBefore = 100 * units.ms
+# showAfter = 100 * units.ms
+
+# simStepSize = 0.1 * units.ms
+# simDuration = 1500 * units.ms
+# # inputParsName = 'oneSecondPulse'
+# # inputParsName = 'pulseTrainInt20Dur10'
+# inputParsName = 'pulseTrainInt20Dur16'
+# # inputParsName = 'pulseTrainInt33Dur10'
+# # inputParsName = 'pulseTrainInt33Dur16'
+# showBefore = 500 * units.ms
+# showAfter = 500 * units.ms
+
+DLInt1ModelProps = "DLInt1Aynur"
+
+DLInt1SynapsePropsE = 'DLInt1_syn_try2_e'
+# DLInt1SynapsePropsE = ""
+DLInt1SynapsePropsI = 'DLInt1_syn_try2_i'
+# DLInt1SynapsePropsI = ""
+DLInt1SynapseProps = "".join((DLInt1SynapsePropsE, DLInt1SynapsePropsI))
+
+DLInt2ModelProps = "DLInt2Try2"
+
+DLInt2SynapseProps = 'DLInt2_syn_try2'
+
+DLInt1DLInt2SynProps = "DLInt1_DLInt2_try1"
+
+opDir = os.path.join(homeFolder, DLInt1ModelProps + DLInt2ModelProps,
+                         DLInt1SynapseProps + DLInt2SynapseProps + DLInt1DLInt2SynProps,
+                         inputParsName)
+opFileDLInt1 = os.path.join(opDir, 'SynCurrentTracesDLInt1.png')
+opFileDLInt2 = os.path.join(opDir, 'SynCurrentTracesDLInt2.png')
+OPNixFile = os.path.join(opDir, 'simResWithSynCurrents.h5')
+if os.path.isfile(OPNixFile):
+    ch = input('Results already exist at {}. Delete?(y/n):'.format(OPNixFile))
+    if ch == 'y':
+        os.remove(OPNixFile)
+        if os.path.isfile(opFileDLInt1):
+            os.remove(opFileDLInt1)
+        if os.path.isfile(opFileDLInt2):
+            os.remove(opFileDLInt2)
+    else:
+        sys.exit('User Abort!')
+
+elif not os.path.isdir(opDir):
+    os.makedirs(opDir)
+
+inputPars = getattr(inputParsList, inputParsName)
+
+
+net = Network()
+JO = JOSpikes265(nOutputs=1, simSettleTime=simSettleTime, **inputPars)
+net.add(JO.JOSGG)
+
+DLInt1PropsDict = getattr(AdExpPars, DLInt1ModelProps)
+dlint1 = VSNeuron(**AdExp, inits=DLInt1PropsDict, name='dlint1')
+dlint1.recordSpikes()
+dlint1.recordMembraneV()
+
+if DLInt1SynapsePropsE:
+    synPropsEDLInt1 = getattr(synapsePropsList, DLInt1SynapsePropsE)
+    dlint1.addSynapse(synName="ExiJO", sourceNG=JO.JOSGG, **exp2Syn,
+                          synParsInits=synPropsEDLInt1,
+                          synStateInits=exp2SynStateInits,
+                          sourceInd=0, destInd=0
+                          )
+if DLInt1SynapsePropsI:
+    synPropsIDLInt1 = getattr(synapsePropsList, DLInt1SynapsePropsI)
+    dlint1.addSynapse(synName="InhJO", sourceNG=JO.JOSGG, **exp2Syn,
+                          synParsInits=synPropsIDLInt1,
+                          synStateInits=exp2SynStateInits,
+                          sourceInd=0, destInd=0
+                          )
+
+dlint1.addToNetwork(net)
+
+if DLInt1SynapsePropsE:
+    gEMonitorDLInt1 = StateMonitor(dlint1.incomingSynapses["ExiJO"], "g_ExiJO", record=True)
+    net.add(gEMonitorDLInt1)
+
+if DLInt1SynapsePropsI:
+    gIMonitorDLInt1 = StateMonitor(dlint1.incomingSynapses["InhJO"], "g_InhJO", record=True)
+    net.add(gIMonitorDLInt1)
+
+DLInt2PropsDict = getattr(AdExpPars, DLInt2ModelProps)
+dlint2 = VSNeuron(**AdExp, inits=DLInt2PropsDict, name='dlint2')
+dlint2.recordMembraneV()
+dlint2.recordSpikes()
+
+if DLInt2SynapseProps:
+    synPropsEDLInt2 = getattr(synapsePropsList, DLInt2SynapseProps)
+    dlint2.addSynapse(synName="ExiJO", sourceNG=JO.JOSGG, **exp2Syn,
+                          synParsInits=synPropsEDLInt2,
+                          synStateInits=exp2SynStateInits,
+                          sourceInd=0, destInd=0
+                          )
+
+if DLInt1DLInt2SynProps:
+    synPropsIDLInt2 = getattr(synapsePropsList, DLInt1DLInt2SynProps)
+    dlint2.addSynapse(synName="DLInt1", sourceNG=dlint1.ng, **exp2Syn,
+                          synParsInits=synPropsIDLInt2,
+                          synStateInits=exp2SynStateInits,
+                          sourceInd=0, destInd=0
+                          )
+
+
+dlint2.addToNetwork(net)
+
+if DLInt2SynapseProps:
+    gEMonitorDLInt2 = StateMonitor(dlint2.incomingSynapses["ExiJO"], "g_ExiJO", record=True)
+    net.add(gEMonitorDLInt2)
+
+if DLInt1DLInt2SynProps:
+    gIMonitorDLInt2 = StateMonitor(dlint2.incomingSynapses["DLInt1"], "g_DLInt1", record=True)
+    net.add(gIMonitorDLInt2)
+
+defaultclock.dt = simStepSize
+totalSimDur = simDuration + simSettleTime
+net.run(totalSimDur, report='text')
+
+simT, DLInt2memV = dlint2.getMemVTrace()
+DLInt2spikeTimes = dlint2.getSpikes()
+
+dlint2MemVAS = AnalogSignal(signal=DLInt2memV / units.mV,
+                            sampling_period=(simStepSize / units.ms) * qu.ms,
+                            t_start=0 * qu.mV,
+                            units="mV",
+                            name="DLInt2 MemV")
+
+dlint2SpikesQU = (DLInt2spikeTimes / units.ms) * qu.ms
+
+simT, DLInt1memV = dlint1.getMemVTrace()
+DLInt1spikeTimes = dlint1.getSpikes()
+
+dlint1MemVAS = AnalogSignal(signal=DLInt1memV / units.mV,
+                            sampling_period=(simStepSize / units.ms) * qu.ms,
+                            t_start=0 * qu.mV,
+                            units="mV",
+                            name="DLInt1 MemV")
+
+dlint1SpikesQU = (DLInt1spikeTimes / units.ms) * qu.ms
+
+
+joSpikesQU = (JO.spikeTimes / units.ms) * qu.ms
+
+sineInput = getSineInput(simDur=simDuration, simStepSize=simStepSize,
+                         sinPulseDurs=inputPars['sinPulseDurs'],
+                         sinPulseStarts=inputPars['sinPulseStarts'],
+                         freq=265 * units.Hz, simSettleTime=simSettleTime)
+
+inputAS = AnalogSignal(signal=sineInput,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="um",
+                                name="Input Vibration Signal")
+
+fig, axs = plt.subplots(nrows=3, figsize=(10, 6.25), sharex='col')
+axs[0].plot(simT / units.ms, DLInt2memV / units.mV)
+spikesY = DLInt2memV.min() + 1.05 * (DLInt2memV.max() - DLInt2memV.min())
+axs[0].plot(DLInt2spikeTimes / units.ms, [spikesY / units.mV] * DLInt2spikeTimes.shape[0], 'k^')
+axs[0].set_ylabel('DLInt2\nMembrane\nPotential\n(mV)')
+axs[0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+
+fig1, axs1 = plt.subplots(nrows=3, figsize=(10, 6.25), sharex='col')
+axs1[0].plot(simT / units.ms, DLInt1memV / units.mV)
+spikesY = DLInt1memV.min() + 1.05 * (DLInt1memV.max() - DLInt1memV.min())
+axs1[0].plot(DLInt1spikeTimes / units.ms, [spikesY / units.mV] * DLInt1spikeTimes.shape[0], 'k^')
+axs1[0].set_ylabel('DLInt1\nMembrane\nPotential\n(mV)')
+axs1[0].set_xlim([(simSettleTime - showBefore) / units.ms,
+                     (totalSimDur + showAfter) / units.ms])
+
+if DLInt2SynapseProps:
+    gSynEDLInt2 = gEMonitorDLInt2.g_ExiJO[0]
+    iSynEDLInt2 = -gSynEDLInt2 * (DLInt2memV - synPropsEDLInt2['Esyn'])
+    axs[1].plot(simT / units.ms,
+                iSynEDLInt2 / units.nA, 'm-', label=r'$I_{synE}$')
+    iSynEASDLInt2 = AnalogSignal(signal=iSynEDLInt2 / units.nA,
+                           sampling_period=(simStepSize / units.ms) * qu.ms,
+                           t_start=0 * qu.mV,
+                           units="nA",
+                           name="DL-Int-2 input EPSC")
+
+if DLInt1DLInt2SynProps:
+    gSynI = gIMonitorDLInt2.g_DLInt1[0]
+    iSynI = -gSynI * (DLInt2memV - synPropsIDLInt2['Esyn'])
+    axs[1].plot(simT / units.ms,
+                iSynI / units.nA, 'c-', label=r'$I_{synI}$')
+    iSynIASDLInt2 = AnalogSignal(signal=iSynI / units.nA,
+                           sampling_period=(simStepSize / units.ms) * qu.ms,
+                           t_start=0 * qu.mV,
+                           units="nA",
+                           name="DL-Int-2 input IPSC")
+
+axs[1].legend(loc='center right')
+axs[1].set_ylabel("Synaptic\ncurrents\n(nA)")
+
+axs[2].plot(simT / units.ms, sineInput,
+            color=[130. / 255, 72. / 255, 7. / 255], ls='-', marker='None',
+            label='Vibration Input')
+axs[2].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+        label='JO Spikes')
+axs[2].legend(loc='center right')
+axs[2].set_xlabel('time (ms)')
+axs[2].set_ylabel('Input')
+fig.tight_layout()
+fig.canvas.draw()
+# plt.show()
+fig.savefig(opFileDLInt2, dpi=150)
+
+if DLInt1SynapsePropsE:
+    gSynEDLInt1 = gEMonitorDLInt1.g_ExiJO[0]
+    iSynEDLInt1 = -gSynEDLInt1 * (DLInt1memV - synPropsEDLInt1['Esyn'])
+    axs1[1].plot(simT / units.ms,
+                iSynEDLInt1 / units.nA, 'r-', label=r'$I_{synE}$')
+    iSynEASDLInt1 = AnalogSignal(signal=iSynEDLInt1 / units.nA,
+                                sampling_period=(simStepSize / units.ms) * qu.ms,
+                                t_start=0 * qu.mV,
+                                units="nA",
+                                name="DL-Int-1 input EPSC")
+
+if DLInt1SynapsePropsI:
+    gSynIDLInt1 = gIMonitorDLInt1.g_InhJO[0]
+    iSynIDLInt1 = -gSynIDLInt1 * (DLInt1memV - synPropsIDLInt1['Esyn'])
+    axs1[1].plot(simT / units.ms,
+                iSynIDLInt1 / units.nA, 'g-', label=r'$I_{synI}$')
+    iSynIASDLInt1 = AnalogSignal(signal=iSynIDLInt1 / units.nA,
+                           sampling_period=(simStepSize / units.ms) * qu.ms,
+                           t_start=0 * qu.mV,
+                           units="nA",
+                           name="DL-Int-1 input IPSC")
+
+
+axs1[1].legend(loc='center right')
+axs1[1].set_ylabel("Synaptic\ncurrents\n(nA)")
+
+axs1[2].plot(simT / units.ms, sineInput,
+            color=[130. / 255, 72. / 255, 7. / 255], ls='-', marker='None',
+            label='Vibration Input')
+axs1[2].plot(JO.spikeTimes / units.ms, [sineInput.max() * 1.05] * len(JO.spikeTimes), 'k^',
+        label='JO Spikes')
+axs1[2].legend(loc='center right')
+axs1[2].set_xlabel('time (ms)')
+axs1[2].set_ylabel('Input')
+fig1.tight_layout()
+fig1.canvas.draw()
+# plt.show()
+fig1.savefig(opFileDLInt1, dpi=150)
+
+nixFile = nixio.File.open(OPNixFile, mode=nixio.FileMode.ReadWrite)
+neuronModels = nixFile.create_section("Neuron Models", "Model Parameters")
+
+DLInt1PropsSec = neuronModels.create_section("DL-Int-1", "AdExp")
+
+for propName, propVal in DLInt1PropsDict.items():
+    addBrianQuantity2Section(DLInt1PropsSec, propName, propVal)
+
+DLInt2PropsSec = neuronModels.create_section("DL-Int-2", "AdExp")
+
+for propName, propVal in DLInt2PropsDict.items():
+    addBrianQuantity2Section(DLInt2PropsSec, propName, propVal)
+
+inputSec = nixFile.create_section("Input Parameters", "Sinusoidal Pulses")
+
+for parName, parVal in inputPars.items():
+    addBrianQuantity2Section(inputSec, parName, parVal)
+
+addBrianQuantity2Section(inputSec, "simSettleTime", simSettleTime)
+
+brianSimSettingsSec = nixFile.create_section("Simulation Parameters", "Brian Simulation")
+addBrianQuantity2Section(brianSimSettingsSec, "simStepSize", simStepSize)
+addBrianQuantity2Section(brianSimSettingsSec, "totalSimDuration", totalSimDur)
+brianSimSettingsSec.create_property("method", nixio.Value("euler"))
+
+
+synPropsSec = nixFile.create_section("Synapse Models", "Model Parameters")
+
+if DLInt1SynapsePropsE:
+
+    JODLInt1SynESec = synPropsSec.create_section("JODLInt1Exi", "DoubleExpSyn")
+    JODLInt1SynEDict = getattr(synapsePropsList, DLInt1SynapsePropsE)
+
+    for propName, propVal in JODLInt1SynEDict.items():
+        addBrianQuantity2Section(JODLInt1SynESec, propName, propVal)
+
+    JODLInt1SynESec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+    JODLInt1SynESec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+
+if DLInt1SynapsePropsI:
+
+    JODLInt1SynISec = synPropsSec.create_section("JODLInt1Inh", "DoubleExpSyn")
+    JODLInt1SynIDict = getattr(synapsePropsList, DLInt1SynapsePropsI)
+
+    for propName, propVal in JODLInt1SynIDict.items():
+        addBrianQuantity2Section(JODLInt1SynISec, propName, propVal)
+    JODLInt1SynISec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+    JODLInt1SynISec.create_property("PostSynaptic Neuron", nixio.Value("DLInt1"))
+
+if DLInt2SynapseProps:
+
+    JODLInt2SynESec = synPropsSec.create_section("JODLInt2Exi", "DoubleExpSyn")
+    JODLInt2SynEDict = getattr(synapsePropsList, DLInt2SynapseProps)
+
+    for propName, propVal in JODLInt2SynEDict.items():
+        addBrianQuantity2Section(JODLInt2SynESec, propName, propVal)
+    JODLInt2SynESec.create_property("PreSynaptic Neuron", nixio.Value("JO"))
+    JODLInt2SynESec.create_property("PostSynaptic Neuron", nixio.Value("DLInt2"))
+
+if DLInt1DLInt2SynProps:
+
+    DLInt1DLInt2SynSec = synPropsSec.create_section("DLInt1DLInt2Inh", "DoubleExpSyn")
+    DLInt1DLInt2SynDict = getattr(synapsePropsList, DLInt1DLInt2SynProps)
+
+    for propName, propVal in DLInt1DLInt2SynDict.items():
+        addBrianQuantity2Section(DLInt1DLInt2SynSec, propName, propVal)
+    DLInt1DLInt2SynSec.create_property("PreSynaptic Neuron", nixio.Value("DLInt1"))
+    DLInt1DLInt2SynSec.create_property("PostSynaptic Neuron", nixio.Value("DLInt2"))
+
+blk = nixFile.create_block("Simulation Traces", "Brian Output")
+DLInt2DA = addAnalogSignal2Block(blk, dlint2MemVAS)
+DLInt1DA = addAnalogSignal2Block(blk, dlint1MemVAS)
+inputDA = addAnalogSignal2Block(blk, inputAS)
+if DLInt1SynapsePropsE:
+    epscAS = addAnalogSignal2Block(blk, iSynEASDLInt1)
+if DLInt1SynapsePropsI:
+    ipscAS = addAnalogSignal2Block(blk, iSynIASDLInt1)
+if DLInt2SynapseProps:
+    epscAS = addAnalogSignal2Block(blk, iSynEASDLInt2)
+if DLInt1DLInt2SynProps:
+    ipscAS = addAnalogSignal2Block(blk, iSynIASDLInt2)
+
+addMultiTag("DLInt2 Spikes", type="Spikes", positions=dlint2SpikesQU,
+            blk=blk, refs=[DLInt2DA])
+addMultiTag("DLInt1 Spikes", type="Spikes", positions=dlint1SpikesQU,
+            blk=blk, refs=[DLInt2DA])
+addMultiTag("JO Spikes", type="Spikes", positions=joSpikesQU,
+            blk=blk, refs=[inputDA])
+
+nixFile.close()