class SnuddaCalibrateSynapses(object):
def __init__(self,networkFile,
preType,postType,
curInj = 10e-9,
holdV = -80e-3,
maxDist = 50e-6,
logFile=None):
if(os.path.isdir(networkFile)):
self.networkFile = networkFile + "/network-synapses.hdf5"
else:
self.networkFile = networkFile
self.preType = preType
self.postType = postType
self.curInj = curInj
self.holdV = holdV
self.logFile = logFile
self.maxDist = maxDist
print("Checking depolarisation/hyperpolarisation of " + preType \
+ " to " + postType + "synapses")
self.injSpacing = 0.2 # 0.5
self.injDuration = 1e-3
# Voltage file
self.voltFile = os.path.dirname(networkFile) \
+ "/synapse-calibration-volt-" \
+ self.preType + "-" + self.postType + ".txt"
self.voltFileAltMask = os.path.dirname(networkFile) \
+ "/synapse-calibration-volt-" \
+ self.preType + "-*.txt"
self.neuronNameRemap = {"FSN" : "FS"}
############################################################################
def neuronName(self,neuronType):
if(neuronType in self.neuronNameRemap):
return self.neuronNameRemap[neuronType]
else:
return neuronType
############################################################################
def setup(self,simName,expType,nMSD1=120,nMSD2=120,nFS=20,nLTS=0,nChIN=0):
from snudda.init.init import SnuddaInit
configName= simName + "/network-config.json"
cnc = SnuddaInit(struct_def={}, config_file=configName, nChannels=1)
cnc.define_striatum(num_dSPN=nMSD1, num_iSPN=nMSD2, num_FS=nFS, num_LTS=nLTS, num_ChIN=nChIN,
volume_type="slice", side_len=200e-6, slice_depth=150e-6)
dirName = os.path.dirname(configName)
if not os.path.exists(dirName):
os.makedirs(dirName)
cnc.write_json(configName)
print("\n\npython3 snudda.py place " + str(simName))
print("python3 snudda.py detect " + str(simName))
print("python3 snudda.py prune " + str(simName))
print("python3 snudda_cut.py " + str(simName) \
+ '/network-synapses.hdf5 "abs(z)<100e-6"')
print("\nThe last command will pop up a figure and enter debug mode, press ctrl+D in the terminal window after inspecting the plot to continue")
print("\n!!! Remember to compile the mod files: nrnivmodl data/neurons/mechanisms")
print("\nTo run for example dSPN -> iSPN (and dSPN->dSPN) calibration:")
print("mpiexec -n 12 -map-by socket:OVERSUBSCRIBE python3 snudda_calibrate_synapses.py run " + str(expType) + " " + str(simName) + "/network-cut-slice.hdf5 dSPN iSPN")
print("\npython3 snudda_calibrate_synapses.py analyse " + str(expType) + " " + str(simName) + "/network-cut-slice.hdf5 --pre dSPN --post iSPN\npython3 snudda_calibrate_synapses.py analyse " + str(simName) + "/network-cut-slice.hdf5 --pre iSPN --post dSPN")
############################################################################
def setupHoldingVolt(self,holdV=None,simEnd=None):
assert simEnd is not None, \
"setupHoldingVolt: Please set simEnd, for holding current"
if(holdV is None):
holdV = self.holdV
if(holdV is None):
print("Not using holding voltage, skipping.")
return
# Setup vClamps to calculate what holding current will be needed
somaVClamp = []
somaList = [self.snuddaSim.neurons[x].icell.soma[0] \
for x in self.snuddaSim.neurons]
for s in somaList:
vc = neuron.h.SEClamp(s(0.5))
vc.rs = 1e-9
vc.amp1 = holdV*1e3
vc.dur1 = 100
somaVClamp.append((s,vc))
neuron.h.finitialize(holdV*1e3)
neuron.h.tstop = 100
neuron.h.run()
self.holdingIClampList = []
# Setup iClamps
for s,vc in somaVClamp:
cur = float(vc.i)
ic = neuron.h.i_clamp(s(0.5))
ic.amp = cur
ic.dur = 2*simEnd*1e3
self.holdingIClampList.append(ic)
# Remove vClamps
vClamps = None
vc = None
############################################################################
def setGABArev(self,vRevCl):
print("Setting GABA reversal potential to " + str(vRevCl*1e3) + " mV")
for s in self.snuddaSim.synapse_list:
assert s.e == -65, "It should be GABA synapses only that we modify!"
s.e = vRevCl * 1e3
############################################################################
def runSim(self,GABArev):
self.snuddaSim = SnuddaSimulate(network_file=self.networkFile,
input_file=None,
log_file=self.logFile,
disable_gap_junctions=True)
# A current pulse to all pre synaptic neurons, one at a time
self.preID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == self.preType]
# injInfo contains (preID,injStartTime)
self.injInfo = list(zip(self.preID, \
self.injSpacing\
+self.injSpacing*np.arange(0,len(self.preID))))
simEnd = self.injInfo[-1][1] + self.injSpacing
# Set the holding voltage
self.setupHoldingVolt(holdV=self.holdV,simEnd=simEnd)
self.setGABArev(GABArev)
# Add current injections defined in init
for (nid,t) in self.injInfo:
print("Current injection to " + str(nid) + " at " + str(t) + " s")
self.snuddaSim.add_current_injection(neuron_id=nid,
start_time=t,
end_time=t + self.injDuration,
amplitude=self.curInj)
# !!! We could maybe update code so that for postType == "ALL" we
# record voltage from all neurons
if(self.postType == "ALL"):
self.snuddaSim.add_recording()
else:
# Record from all the potential post synaptic neurons
self.snuddaSim.add_recording_of_type(self.postType)
# Also save the presynaptic traces for debugging, to make sure they spike
self.snuddaSim.add_recording_of_type(self.preType)
# Run simulation
self.snuddaSim.run(simEnd * 1e3, hold_v=self.holdV)
# Write results to disk
self.snuddaSim.write_voltage(self.voltFile)
############################################################################
def readVoltage(self,voltFile):
if(not os.path.exists(voltFile)):
print("Missing " + voltFile)
allFile = self.voltFileAltMask.replace("*","ALL")
if(os.path.exists(allFile)):
print("Using " + allFile + " instead")
voltFile = allFile
elif(self.preType == self.postType):
fileList = glob.glob(self.voltFileAltMask)
if(len(fileList) > 0):
voltFile = fileList[0]
print("Using " + voltFile + " instead, since pre and post are same")
else:
print("Aborting")
exit(-1)
data = np.genfromtxt(voltFile, delimiter=',')
assert(data[0,0] == -1) # First column should be time
time = data[0,1:] / 1e3
voltage = dict()
for rows in data[1:,:]:
cID = int(rows[0])
voltage[cID] = rows[1:] * 1e-3
return (time,voltage)
############################################################################
# This extracts all the voltage deflections, to see how strong they are
def analyse(self,expType,maxDist=None,nMaxShow=10):
self.setupExpData()
if(maxDist is None):
maxDist = self.maxDist
# Read the data
self.snuddaLoad = SnuddaLoad(self.networkFile)
self.data = self.snuddaLoad.data
time,voltage = self.readVoltage(self.voltFile) # sets self.voltage
checkWidth = 0.05
# Generate current info structure
# A current pulse to all pre synaptic neurons, one at a time
self.preID = [x["neuronID"] \
for x in self.data["neurons"] \
if x["type"] == self.preType]
self.possiblePostID = [x["neuronID"] \
for x in self.data["neurons"] \
if x["type"] == self.postType]
# injInfo contains (preID,injStartTime)
self.injInfo = zip(self.preID, \
self.injSpacing\
+self.injSpacing*np.arange(0,len(self.preID)))
# For each pre synaptic neuron, find the voltage deflection in each
# of its post synaptic neurons
synapseData = []
tooFarAway = 0
for (preID,t) in self.injInfo:
# Post synaptic neuron to preID
synapses,coords = self.snuddaLoad.find_synapses(pre_id=preID)
postIDset = set(synapses[:,1]).intersection(self.possiblePostID)
prePos = self.snuddaLoad.data["neuronPositions"][preID,:]
for postID in postIDset:
if(maxDist is not None):
postPos = self.snuddaLoad.data["neuronPositions"][postID,:]
if(np.linalg.norm(prePos-postPos) > maxDist):
tooFarAway += 1
continue
# There is a bit of synaptic delay, so we can take voltage
# at first timestep as baseline
tIdx = np.where(np.logical_and(t <= time, time <= t + checkWidth))[0]
synapseData.append((time[tIdx],voltage[postID][tIdx]))
if(maxDist is not None):
print("Number of pairs excluded, distance > " \
+ str(maxDist*1e6) + "mum : " + str(tooFarAway))
# Fig names:
traceFig = os.path.dirname(self.networkFile) \
+ "/figures/" + expType +"synapse-calibration-volt-traces-" \
+ self.preType + "-" + self.postType + ".pdf"
histFig = os.path.dirname(self.networkFile) \
+ "/figures/" + expType + "synapse-calibration-volt-histogram-" \
+ self.preType + "-" + self.postType + ".pdf"
figDir = os.path.dirname(self.networkFile) + "/figures"
if(not os.path.exists(figDir)):
os.makedirs(figDir)
# Extract the amplitude of all voltage pulses
amp = np.zeros((len(synapseData),))
idxMax = np.zeros((len(synapseData),),dtype=int)
tMax = np.zeros((len(synapseData),))
for i,(t,v) in enumerate(synapseData):
# Save the largest deflection -- with sign
idxMax[i] = np.argmax(np.abs(v-v[0]))
tMax[i] = t[idxMax[i]] - t[0]
amp[i] = v[idxMax[i]]-v[0]
assert len(amp) > 0, "No responses... too short distance!"
print("Min amp: " + str(np.min(amp)))
print("Max amp: " + str(np.max(amp)))
print("Mean amp: " + str(np.mean(amp)) + " +/- " + str(np.std(amp)))
print("Amps: " + str(amp))
# Now we have all synapse deflections in synapseData
matplotlib.rcParams.update({'font.size': 22})
sortIdx = np.argsort(amp)
if(len(sortIdx) > nMaxShow):
keepIdx = [sortIdx[int(np.round(x))] \
for x in np.linspace(0,len(sortIdx)-1,nMaxShow)]
else:
keepIdx = sortIdx
plt.figure()
for x in keepIdx:
t,v = synapseData[x]
plt.plot((t-t[0])*1e3,(v-v[0])*1e3,color="black")
plt.scatter(tMax*1e3,amp*1e3,color="blue",marker=".",s=100)
if((expType,self.preType,self.postType) in self.expData):
expMean,expStd = self.expData[(expType,self.preType,self.postType)]
tEnd = (t[-1]-t[0])*1e3
axes = plt.gca()
ay = axes.get_ylim()
# Plot SD or 1.96 SD?
plt.errorbar(tEnd,expMean,expStd,ecolor="red",
marker='o',color="red")
modelMean = np.mean(amp)*1e3
modelStd = np.std(amp)*1e3
plt.errorbar(tEnd-2,modelMean,modelStd,ecolor="blue",
marker="o",color="blue")
axes.set_ylim(ay)
plt.xlabel("Time (ms)")
plt.ylabel("Voltage (mV)")
#plt.title(str(len(synapseData)) + " traces")
plt.title(self.neuronName(self.preType) \
+ " to " + self.neuronName(self.postType))
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.ion()
plt.show()
plt.savefig(traceFig,dpi=300)
plt.figure()
plt.hist(amp*1e3,bins=20)
plt.title(self.neuronName(self.preType) \
+ " to " + self.neuronName(self.postType))
plt.xlabel("Voltage deflection (mV)")
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.show()
plt.savefig(histFig,dpi=300)
import pdb
pdb.set_trace()
############################################################################
def setupExpData(self):
self.expData = dict()
planertD1D1 = (0.24,0.15)
planertD1D2 = (0.33,0.15)
planertD2D1 = (0.27,0.09)
planertD2D2 = (0.45,0.44)
planertFSD1 = (4.8,4.9)
planertFSD2 = (3.1,4.1)
self.expData[("Planert2010","dSPN","dSPN")] = planertD1D1
self.expData[("Planert2010","dSPN","iSPN")] = planertD1D2
self.expData[("Planert2010","iSPN","dSPN")] = planertD2D1
self.expData[("Planert2010","iSPN","iSPN")] = planertD2D2
self.expData[("Planert2010","FSN","dSPN")] = planertFSD1
self.expData[("Planert2010","FSN","iSPN")] = planertFSD2
class SnuddaModelCurrentInjections(object):
def __init__(self, simName, simType, curInj=10e-9):
self.simName = simName
self.simType = simType
self.snuddaSim = None
self.expDataDict = dict()
self.expTraceDict = dict()
if (simType == "Chuhma2011"):
self.tInj = 0.3
self.injDuration = 1e-3
self.curInj = 10e-9
self.tWindow = 0.03
self.simEnd = self.tInj + self.tWindow * 2
self.holdV = -60e-3
self.GABArev = 2e-3 # 144mM inside, 133.5mM outside, 32-33C --NERNST--> 2mV
self.nNrns = 100 # How many we measure from of each type
elif (simType == "Straub2016FS" or simType == "Straub2016LTS"):
self.tInj = 0.5
self.injDuration = 1e-3
self.curInj = 10e-9
self.tWindow = 0.03
self.simEnd = self.tInj + self.tWindow * 2
self.holdV = -70e-3
self.GABArev = -0.3e-3 # Out: 133.5 mM chloride, In 131.5 mM, Temperature 33-34 C
self.nNrns = 30
elif (simType == "Szydlowski2013"):
assert False, "Szydlowski did not stimulate all FS or did they when recording LTS?"
self.tInj = 0.5
self.injDuration = 1e-3
self.curInj = 10e-9
self.tWindow = 0.03
self.simEnd = self.tInj + self.tWindow * 2
self.holdV = -70e-3
self.GABArev = -30e-3
self.nNrns = 30
else:
print("Unknown simType: " + str(simType))
self.plotExpTrace = False
self.neuronNameRemap = {"FSN": "FS"}
############################################################################
def neuronName(self, neuronType):
if (neuronType in self.neuronNameRemap):
return self.neuronNameRemap[neuronType]
else:
return neuronType
############################################################################
def defineNetwork(self, simName, simType=None):
if (simType is None):
simType = self.simType
configName = simName + "/network-config.json"
cnc = SnuddaInit(struct_def={}, config_file=configName, nChannels=1)
# In a 1x1x0.15 mm slice there are 12000 neurons normally
# We want 10% of MS population only, since those are the ones being
# stimulated (Chuhma et al 2011)
#
# 47.5% dSPN normally, now 4.75% of normal density = 570 dSPN, 570 iSPN
# We assume we measure only monosynaptic connections.
#
# Adding 10 FSN, 10 ChIN, 10 dSPN, 10 iSPN to measure from
#
if (False):
#Small debug version
#cnc.defineStriatum(nMSD1=20,nMSD2=20,nFS=0,nLTS=0,nChIN=0,
# volumeType="slice",sideLen=200e-6)
#cnc.defineStriatum(nMSD1=20,nMSD2=20,nFS=10,nLTS=0,nChIN=10,
# volumeType="slice",sideLen=200e-6)
cnc.define_striatum(num_dSPN=153,
num_iSPN=153,
num_FS=10,
num_LTS=0,
num_ChIN=10,
volume_type="slice",
side_len=500e-6)
if (simType == "Chuhma2011"):
cnc.define_striatum(
num_dSPN=1140 + self.nNrns,
num_iSPN=1140 + self.nNrns,
num_FS=5,
num_LTS=0,
num_ChIN=self.nNrns,
volume_type="slice",
side_len=1000e-6,
slice_depth=300e-6) # 400mum, assume 100 mum dead
elif (simType == "Straub2016FS"):
# nFS must be correct density, but readout neurons can be any density
cnc.define_striatum(
num_dSPN=self.nNrns,
num_iSPN=self.nNrns,
num_FS=182,
num_LTS=0,
num_ChIN=self.nNrns,
volume_type="slice",
side_len=1000e-6,
slice_depth=175e-6) #275e-6 m slice, assume 100e-6 dead
elif (simType == "Straub2016LTS"):
cnc.define_striatum(num_dSPN=self.nNrns,
num_iSPN=self.nNrns,
num_FS=0,
num_LTS=98,
num_ChIN=self.nNrns,
volume_type="slice",
side_len=1000e-6,
slice_depth=175e-6)
elif (simType == "Szydlowski2013"):
cnc.define_striatum(num_dSPN=0,
num_iSPN=0,
num_FS=156,
num_LTS=self.nNrns,
num_ChIN=0,
volume_type="slice",
side_len=1000e-6,
slice_depth=150e-6)
else:
print("setup : Unkown simType: " + str(simType))
exit(-1)
dirName = os.path.dirname(configName)
if not os.path.exists(dirName):
os.makedirs(dirName)
cnc.write_json(configName)
############################################################################
def simulateNetwork(self, simName, simType=None):
if (simType is None):
simType = self.simType
if (simType == "Chuhma2011"):
self.simulateNetworkChuhma2011(simName)
elif (simType == "Straub2016FS" or simType == "Straub2016LTS"):
self.simulateNetworkStraub2016(simName, simType)
elif (simType == "Szydlowski2013"):
self.simulateNetworkSzydlowski2013(simName)
else:
print("simulateNetwork: unknown simType = " + str(simType))
exit(-1)
############################################################################
def simulateNetworkChuhma2011(self, simName):
simType = "Chuhma2011"
if (self.snuddaSim is None):
logFile = simName + "/log/simlog.txt"
cutFile = simName + "/network-cut-slice.hdf5"
if (os.path.exists(cutFile)):
self.networkFile = cutFile
else:
self.networkFile = simName + "/network-synapses.hdf5"
print("Using network file: " + str(self.networkFile))
self.snuddaSim = SnuddaSimulate(network_file=self.networkFile,
input_file=None,
log_file=logFile,
disable_gap_junctions=True)
# Get neuronID of neurons that will get artificial stimulation
stimID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if "SPN" in x["type"]]
# Pick neurons that we will measure from
measureFSN = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "FSN"]
measureChIN = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "ChIN"]
# For future: maybe pick the ones more centrally
measuredSPN = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "dSPN"][:self.nNrns]
measureiSPN = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "iSPN"][:self.nNrns]
# Remove the overlap, ie dont stimulate the neurons we measure from
stimID = np.setdiff1d(stimID, np.union1d(measuredSPN, measureiSPN))
measureID = np.union1d(np.union1d(measuredSPN, measureiSPN),
np.union1d(measureFSN, measureChIN))
self._simulateNetworkHelper(simName, simType, stimID, measureID)
############################################################################
def simulateNetworkStraub2016(self, simName, simType):
if (self.snuddaSim is None):
logFile = simName + "/log/simlog.txt"
self.networkFile = simName + "/network-synapses.hdf5"
self.snuddaSim = SnuddaSimulate(network_file=self.networkFile,
input_file=None,
log_file=logFile,
disable_gap_junctions=True)
# Get neuronID of neurons that will get artificial stimulation
if (simType == "Straub2016FS"):
stimID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if "FSN" in x["type"]]
elif (simType == "Straub2016LTS"):
stimID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if "LTS" in x["type"]]
else:
print("simulateNetworkStraub2016: Unknown simType : " + simType)
exit(-1)
measureID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "ChIN" \
or x["type"] == "dSPN" \
or x["type"] == "iSPN"]
self._simulateNetworkHelper(simName, simType, stimID, measureID)
############################################################################
def simulateNetworkSzydlowski2013(self, simName):
if (self.snuddaSim is None):
logFile = simName + "/log/simlog.txt"
self.networkFile = simName + "/network-synapses.hdf5"
self.snuddaSim = SnuddaSimulate(network_file=self.networkFile,
input_file=None,
log_file=logFile,
disable_gap_junctions=True)
stimID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if "FSN" in x["type"]]
measureID = [x["neuronID"] \
for x in self.snuddaSim.network_info["neurons"] \
if x["type"] == "LTS"]
self._simulateNetworkHelper(simName, simType, stimID, measureID)
############################################################################
def _simulateNetworkHelper(self, simName, simType, stimID, measureID):
if (self.snuddaSim is None):
logFile = simName + "/log/simlog.txt"
self.networkFile = simName + "/network-synapses.hdf5"
self.snuddaSim = SnuddaSimulate(network_file=self.networkFile,
input_file=None,
log_file=logFile,
disable_gap_junctions=True)
# Set up stimulation protocol
for nID in stimID:
self.snuddaSim.add_current_injection(neuron_id=nID,
start_time=self.tInj,
end_time=self.tInj +
self.injDuration,
amplitude=self.curInj)
# Add recordings
self.snuddaSim.add_voltage_clamp(cell_id=measureID,
voltage=self.holdV,
duration=self.simEnd,
save_i_flag=True)
# Also add voltage recording for debugging reasons
saveVoltage = True # False #True
if (saveVoltage):
self.snuddaSim.add_recording(cell_id=stimID)
self.setGABArev(self.GABArev)
self.snuddaSim.run(self.simEnd * 1e3)
self.currentFile = simName + "/" + simType \
+ "-network-stimulation-current.txt"
self.snuddaSim.write_current(self.currentFile)
if (saveVoltage):
voltageFile = simName + "/" + simType \
+ "-network-stimulation-voltage.txt"
self.snuddaSim.write_voltage(voltageFile)
############################################################################
def createNetwork(self, simName):
sn = Snudda(simName)
class FakeArgs(object):
def __init__(self):
setattr(self, "h5legacy", "latest")
setattr(self, "volumeID", None)
setattr(self, "hvsize", None) # Use default value
setattr(self, "cont", None)
setattr(self, "mergeonly", False)
args = FakeArgs()
sn.place_neurons(args)
sn.touch_detection(args)
sn.prune_synapses(args)
############################################################################
def setupExpDataDict(self):
self.expDataDict = dict()
# Straub et al 2016
LTS2SPN = np.array([
0.0316, 0.0433, 0.0474, 0.1253, 0.1839, 0.1860, 0.1946, 0.1968,
0.2082, 0.2203, 0.2384, 0.2439, 0.2793, 0.3091, 0.3234, 0.3271,
0.3383, 0.3500, 0.3540, 0.3662, 0.3662, 0.3831, 0.4053, 0.4099,
0.4288, 0.4337, 0.4966, 0.5023, 0.5196, 0.5314, 0.5436, 0.5560,
0.5817, 0.6017, 0.6736, 0.6968, 0.7047, 0.7047, 0.7127, 0.7979,
0.9034, 1.0461
])
LTS2ChIN = np.array([
0.2466, 0.5080, 0.5196, 0.6017, 0.6660, 0.7541, 0.7713, 0.8442,
1.1069, 1.2391, 1.2818, 1.4030, 2.3315
])
FSN2SPN = np.array([
0.3091, 0.5137, 0.5255, 0.5687, 0.6890, 0.8161, 0.8832, 0.8932,
0.9667, 1.0228, 1.0228, 1.0822, 1.1844, 1.2391, 1.2964, 1.3111,
1.4189, 1.4350, 1.5530, 1.6247, 1.7385, 1.7984, 1.9028, 2.1063,
2.2539, 2.3580, 2.4669, 2.4949, 2.5232, 2.7307, 2.7930, 2.8247,
3.2711, 3.3458, 3.4222, 4.2648, 4.4617, 4.9668, 5.3148
])
FSN2ChIN = np.array([0.0233, 0.0378, 0.0419, 0.0428, 0.0666, 0.0762])
self.expDataDict[("Straub2016LTS", "dSPN")] = LTS2SPN
self.expDataDict[("Straub2016LTS", "iSPN")] = LTS2SPN
self.expDataDict[("Straub2016LTS", "ChIN")] = LTS2ChIN
self.expDataDict[("Straub2016FS", "dSPN")] = FSN2SPN
self.expDataDict[("Straub2016FS", "iSPN")] = FSN2SPN
self.expDataDict[("Straub2016FS", "ChIN")] = FSN2ChIN
if (self.plotExpTrace):
self.expTraceDict = dict()
LTS2SPN = np.genfromtxt("DATA/Straub2016/LTSItoSPN_Straub2.txt")
LTS2ChIN = np.genfromtxt("DATA/Straub2016/LTSItoChIN_Straub2.txt")
FS2SPN = np.genfromtxt(
"DATA/Straub2016/FSItoSPN_Straub2_shorter.txt")
# Convert current from pA to nA
LTS2SPN[:, 1:] = 1e-3 * LTS2SPN[:, 1:]
LTS2ChIN[:, 1:] = 1e-3 * LTS2ChIN[:, 1:]
FS2SPN[:, 1:] = 1e-3 * FS2SPN[:, 1:]
self.expTraceDict[("Straub2016LTS", "dSPN")] = LTS2SPN
self.expTraceDict[("Straub2016LTS", "iSPN")] = LTS2SPN
self.expTraceDict[("Straub2016LTS", "ChIN")] = LTS2ChIN
self.expTraceDict[("Straub2016FS", "dSPN")] = FS2SPN
self.expTraceDict[("Straub2016FS", "iSPN")] = FS2SPN
SPN2SPN = np.genfromtxt("DATA/Chuhma2011/SPNtoSPN_Chuhma.txt")
SPN2ChIN = np.genfromtxt("DATA/Chuhma2011/SPNtoChIN_Chuhma.txt")
# Convert current from pA to nA
SPN2SPN[:, 1:] = 1e-3 * SPN2SPN[:, 1:]
SPN2ChIN[:, 1:] = 1e-3 * SPN2ChIN[:, 1:]
self.expTraceDict[("Chuhma2011", "dSPN")] = SPN2SPN
self.expTraceDict[("Chuhma2011", "iSPN")] = SPN2SPN
self.expTraceDict[("Chuhma2011", "ChIN")] = SPN2ChIN
############################################################################
def analyseNetwork(self, simName, simType=None, nPlotMax=10):
figDir = simName + "/figures/"
if (not os.path.exists(figDir)):
os.makedirs(figDir)
if (simType is None):
simType = self.simType
if (simType == "Straub2016LTS"):
preType = "LTS"
self.setupExpDataDict()
elif (simType == "Straub2016FS"):
preType = "FSN"
self.setupExpDataDict()
elif (simType == "Chuhma2011"):
preType = "SPN"
self.setupExpDataDict()
elif (simType == "Szydlowski2013"):
preType = "FSN"
else:
print("Unknown simType : " + simType)
exit(-1)
print("Analysing data in " + simName)
voltFile = simName + "/" + simType + "-network-stimulation-current.txt"
# Read data from file
data = np.genfromtxt(voltFile, delimiter=",")
assert (data[0, 0] == -1) # First column should be time
time = data[0, 1:] * 1e-3
current = dict()
for rows in data[1:, :]:
cID = int(rows[0])
current[cID] = rows[1:] * 1e-9
# Data in time, current now
# Read the network info
networkFile = simName + "/network-synapses.hdf5"
self.snuddaLoad = SnuddaLoad(networkFile)
self.data = self.snuddaLoad.data
recordedNeurons = [x for x in current]
# Group the neurons by type
if (simType == "Chuhma2011"):
neuronTypeList = ["dSPN", "iSPN", "FSN", "ChIN"]
elif (simType == "Straub2016FS" or simType == "Straub2016LTS"):
neuronTypeList = ["dSPN", "iSPN", "ChIN"]
elif (simType == "Szydlowski2013"):
neuronTypeList = ["LTS"]
else:
print("simulate: Unknown simType: " + simType)
exit(-1)
neuronPlotList = []
minTimeIdx = np.where(time > self.tInj)[0][0]
maxTimeIdx = np.where(time > self.tInj + self.tWindow)[0][0]
for nt in neuronTypeList:
IDList = [
x for x in current if self.data["neurons"][x]["type"] == nt
]
maxIdx = [np.argmax(np.abs(current[x][minTimeIdx:maxTimeIdx] \
-current[x][minTimeIdx])) + minTimeIdx \
for x in IDList]
neuronPlotList.append((IDList, maxIdx))
matplotlib.rcParams.update({'font.size': 22})
for plotID, maxIdx in neuronPlotList:
if (len(plotID) == 0):
continue
plotType = self.data["neurons"][plotID[0]]["type"]
figName = figDir + "/" + simType + "-" + plotType + "-current-traces.pdf"
figNameHist = figDir + "/" + simType + "-" + plotType + "-current-histogram.pdf"
goodMax = []
plt.figure()
peakAmp = []
peakTime = []
voltCurve = []
for pID, mIdx in zip(plotID, maxIdx):
tIdx = np.where(
np.logical_and(time > self.tInj,
time < self.tInj + self.tWindow))[0]
curAmp = current[pID][tIdx] - current[pID][tIdx[0] - 1]
maxAmp = current[pID][mIdx] - current[pID][tIdx[0] - 1]
if (mIdx < minTimeIdx or mIdx > maxTimeIdx
or abs(maxAmp) < 1e-12):
# No peaks
continue
goodMax.append(maxAmp * 1e9)
peakAmp.append(maxAmp * 1e9)
peakTime.append((time[mIdx] - time[tIdx[0]]) * 1e3)
voltCurve.append(
((time[tIdx] - time[tIdx[0]]) * 1e3, curAmp * 1e9))
# Pick which curves to plot
sortIdx = np.argsort(peakAmp)
if (len(sortIdx) < nPlotMax):
keepIdx = sortIdx
else:
keepIdx = [sortIdx[int(np.round(x))] for x in \
np.linspace(0,len(sortIdx)-1,nPlotMax)]
for x in keepIdx:
plt.plot(voltCurve[x][0], voltCurve[x][1], 'k-')
plt.scatter(peakTime, peakAmp, marker=".", c="blue", s=100)
nType = self.data["neurons"][plotID[0]]["type"]
if ((simType, nType) in self.expDataDict):
expData = self.expDataDict[(simType, nType)]
t = self.tWindow * 1e3 * (
1 + 0.03 * np.random.rand(expData.shape[0]))
plt.scatter(t, -expData, marker=".", c="red", s=100)
if (self.plotExpTrace and (simType, nType) in self.expTraceDict):
data = self.expTraceDict[(simType, nType)]
tExp = data[:, 0]
vExp = data[:, 1:]
tIdx = np.where(tExp < self.tWindow * 1e3)[0]
plt.plot(tExp[tIdx], vExp[tIdx, :], c="red")
plt.title(
self.neuronName(preType) + " to " + self.neuronName(plotType))
plt.xlabel("Time (ms)")
plt.ylabel("Current (nA)")
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.ion()
plt.show()
plt.savefig(figName, dpi=300)
# Also plot histogram
plt.figure()
plt.hist(goodMax)
plt.xlabel("Current (nA)")
plt.title(
self.neuronName(preType) + " to " + self.neuronName(plotType))
# Remove part of the frame
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.tight_layout()
plt.ion()
plt.show()
plt.savefig(figNameHist, dpi=300)
import pdb
pdb.set_trace()
############################################################################
def setGABArev(self, vRevCl):
print("Setting GABA reversal potential to " + str(vRevCl * 1e3) +
" mV")
for s in self.snuddaSim.synapse_list:
assert s.e == -65, "It should be GABA synapses only that we modify!"
s.e = vRevCl * 1e3