#print allpopMUAs
#print "\n"
normte = np.zeros(len(popsizes))
# For each population, obtain its MUA against all other populations
for popnum in range(len(allpopMUAs)):
allotherpops = copy.deepcopy(allpopMUAs)
thispop = allotherpops.pop(
popnum
) # Gives us this population, AND removes it from 'remaining' list
thispopvec = h.Vector(thispop) # Convert thispop to a hoc Vector
# Now, for each 'other' population, compare it to 'thispop'
for otherpop in allotherpops:
# Convert otherpop to a hoc Vector
otherpopvec = h.Vector(otherpop)
normtevec = h.normte(thispopvec, otherpopvec,
30) # Try 30 shuffles
#normtevec.printf("%8.4f\n")
#print "using %f, popnum %d" % (normtevec.x[2], popnum)
normte[popnum] += normtevec.x[2]
#print normte
#print "\n"
allnormtes[runnum] = normte
print "allnormtes"
print allnormtes
xaxis = range(len(base_popsizes))
meanperpop = np.mean(allnormtes, axis=0)
stdperpop = np.std(allnormtes, axis=0)
print meanperpop
print stdperpop
def nTE(cells1=[],
cells2=[],
spks1=None,
spks2=None,
timeRange=None,
binSize=20,
numShuffle=30):
'''
Calculate normalized transfer entropy
- cells1 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 1 (default: [])
- cells2 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 1 (default: [])
- spks1 (list): Spike train 1; list of spike times; if omitted then obtains spikes from cells1 (default: None)
- spks2 (list): Spike train 2; list of spike times; if omitted then obtains spikes from cells2 (default: None)
- timeRange ([min, max]): Range of time to calculate nTE in ms (default: [0,cfg.duration])
- binSize (int): Bin size used to convert spike times into histogram
- numShuffle (int): Number of times to shuffle spike train 1 to calculate TEshuffled; note: nTE = (TE - TEShuffled)/H(X2F|X2P)
- Returns nTE (float): normalized transfer entropy
'''
from neuron import h
import netpyne
from .. import sim
import os
root = os.path.dirname(netpyne.__file__)
if 'nte' not in dir(h):
try:
print(
' Warning: support/nte.mod not compiled; attempting to compile from %s via "nrnivmodl support"'
% (root))
os.system('cd ' + root + '; nrnivmodl support')
from neuron import load_mechanisms
load_mechanisms(root)
print(' Compilation of support folder mod files successful')
except:
print(' Error compiling support folder mod files')
return
h.load_file(root +
'/support/nte.hoc') # nTE code (also requires support/net.mod)
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells1)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks1 = list(spkts)
if not spks2: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells2)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks2 = list(spkts)
# time range
if getattr(sim, 'cfg', None):
timeRange = [0, sim.cfg.duration]
else:
timeRange = [0, max(spks1 + spks2)]
inputVec = h.Vector()
outputVec = h.Vector()
histo1 = np.histogram(spks1,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount1 = histo1[0]
histo2 = np.histogram(spks2,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount2 = histo2[0]
inputVec.from_python(histoCount1)
outputVec.from_python(histoCount2)
out = h.normte(inputVec, outputVec, numShuffle)
TE, H, nTE, _, _ = out.to_python()
return nTE
def nTE(cells1=[],
cells2=[],
spks1=None,
spks2=None,
timeRange=None,
binSize=20,
numShuffle=30):
"""Calculate normalized transfer entropy.
Parameters
----------
cells1 : list
Subset of cells from which to obtain spike train 1.
**Default:** ``[]``
**Options:**
``['all']`` plots all cells and stimulations,
``['allNetStims']`` plots just stimulations,
``['popName1']`` plots a single population,
``['popName1', 'popName2']`` plots multiple populations,
``[120]`` plots a single cell,
``[120, 130]`` plots multiple cells,
``[('popName1', 56)]`` plots a cell from a specific population,
``[('popName1', [0, 1]), ('popName2', [4, 5, 6])]``, plots cells from multiple populations
cells2 : list
Subset of cells from which to obtain spike train 2.
**Default:** ``[]``
**Options:** same as for `cells1`
spks1 : list
Spike train 1; list of spike times; if omitted then obtains spikes from cells1.
**Default:** ``None``
spks2 : list
Spike train 2; list of spike times; if omitted then obtains spikes from cells2.
**Default:** ``None``
timeRange : list [min, max]
Range of time to calculate nTE in ms.
**Default:** ``None`` uses the entire simulation time range
binSize : int
Bin size used to convert spike times into histogram.
**Default:** ``20``
numShuffle : int
Number of times to shuffle spike train 1 to calculate TEshuffled; note: nTE = (TE - TEShuffled)/H(X2F|X2P).
**Default:** ``30``
Returns
-------
float
Normalized transfer entropy
See Also
--------
granger :
Examples
--------
>>> import netpyne, netpyne.examples.example
>>> out = netpyne.analysis.nTE()
"""
from neuron import h
import netpyne
from .. import sim
import os
root = os.path.dirname(netpyne.__file__)
if 'nte' not in dir(h):
try:
print(
' Warning: support/nte.mod not compiled; attempting to compile from %s via "nrnivmodl support"'
% (root))
os.system('cd ' + root + '; nrnivmodl support')
from neuron import load_mechanisms
load_mechanisms(root)
print(' Compilation of support folder mod files successful')
except:
print(' Error compiling support folder mod files')
return
h.load_file(root +
'/support/nte.hoc') # nTE code (also requires support/net.mod)
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells1)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks1 = list(spkts)
if not spks2: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells2)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks2 = list(spkts)
# time range
if getattr(sim, 'cfg', None):
timeRange = [0, sim.cfg.duration]
else:
timeRange = [0, max(spks1 + spks2)]
inputVec = h.Vector()
outputVec = h.Vector()
histo1 = np.histogram(spks1,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount1 = histo1[0]
histo2 = np.histogram(spks2,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount2 = histo2[0]
inputVec.from_python(histoCount1)
outputVec.from_python(histoCount2)
out = h.normte(inputVec, outputVec, numShuffle)
TE, H, nTE, _, _ = out.to_python()
return nTE
def nTE(cells1=[],
cells2=[],
spks1=None,
spks2=None,
timeRange=None,
binSize=20,
numShuffle=30):
"""
Function that calculates the Normalized Transfer Entropy (nTE) between two spike train signals.
Transfer entropy is a model-free statistic that is able to measure
the time-directed transfer of information between stochastic variables
and therefore provides an asymmetric method to measure information transfer.
In simple words, the nTE represents the fraction of information in X explained
by its own past which is not explained by the past of Y.
Kale, P. et al (2018, July). Normalized Transfer Entropy as a Tool to Identify Multisource
Functional Epileptic Networks IEEE Engineering in Medicine and Biology Society (EMBC)
https://doi.org/10.1109/embc.2018.8512532
Parameters
----------
cells1 : list
Subset of cells from which to obtain spike train 1.
**Default:** ``[]``
**Options:**
``['all']`` plots all cells and stimulations,
``['allNetStims']`` plots just stimulations,
``['popName1']`` plots a single population,
``['popName1', 'popName2']`` plots multiple populations,
``[120]`` plots a single cell,
``[120, 130]`` plots multiple cells,
``[('popName1', 56)]`` plots a cell from a specific population,
``[('popName1', [0, 1]), ('popName2', [4, 5, 6])]``, plots cells from multiple populations
cells2 : list
Subset of cells from which to obtain spike train 2.
**Default:** ``[]``
**Options:** same as for `cells1`
spks1 : list
Spike train 1; list of spike times; if omitted then obtains spikes from cells1.
**Default:** ``None``
**Options:** ``<option>`` <description of option>
spks2 : list
Spike train 2; list of spike times; if omitted then obtains spikes from cells2.
**Default:** ``None``
**Options:** ``<option>`` <description of option>
timeRange : list [min, max]
Range of time to calculate nTE in ms.
**Default:** ``None`` uses the entire simulation time range
**Options:** ``<option>`` <description of option>
binSize : int
Bin size used to convert spike times into histogram.
**Default:** ``20``
**Options:** ``<option>`` <description of option>
numShuffle : int
Number of times to shuffle spike train 1 to calculate TEshuffled; note: nTE = (TE - TEShuffled)/H(X2F|X2P).
**Default:** ``30``
**Options:** ``<option>`` <description of option>
Returns
-------
"""
from neuron import h
import netpyne
from .. import sim
import os
root = os.path.dirname(netpyne.__file__)
if 'nte' not in dir(h):
try:
print(
' Warning: support/nte.mod not compiled; attempting to compile from %s via "nrnivmodl support"'
% (root))
os.system('cd ' + root + '; nrnivmodl support')
from neuron import load_mechanisms
load_mechanisms(root)
print(' Compilation of support folder mod files successful')
except:
print(' Error compiling support folder mod files')
return
h.load_file(root +
'/support/nte.hoc') # nTE code (also requires support/net.mod)
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells1)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks1 = list(spkts)
if not spks2: # if doesnt contain a list of spk times, obtain from cells specified
cells, cellGids, netStimPops = getCellsInclude(cells2)
numNetStims = 0
# Select cells to include
if len(cellGids) > 0:
try:
spkts = [
spkt for spkgid, spkt in zip(sim.allSimData['spkid'],
sim.allSimData['spkt'])
if spkgid in cellGids
]
except:
spkts = []
else:
spkts = []
# Add NetStim spikes
spkts = list(spkts)
numNetStims = 0
for netStimPop in netStimPops:
if 'stims' in sim.allSimData:
cellStims = [
cellStim
for cell, cellStim in sim.allSimData['stims'].items()
if netStimPop in cellStim
]
if len(cellStims) > 0:
spktsNew = [
spkt for cellStim in cellStims
for spkt in cellStim[netStimPop]
]
spkts.extend(spktsNew)
numNetStims += len(cellStims)
spks2 = list(spkts)
# time range
if getattr(sim, 'cfg', None):
timeRange = [0, sim.cfg.duration]
else:
timeRange = [0, max(spks1 + spks2)]
inputVec = h.Vector()
outputVec = h.Vector()
histo1 = np.histogram(spks1,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount1 = histo1[0]
histo2 = np.histogram(spks2,
bins=np.arange(timeRange[0], timeRange[1], binSize))
histoCount2 = histo2[0]
inputVec.from_python(histoCount1)
outputVec.from_python(histoCount2)
out = h.normte(inputVec, outputVec, numShuffle)
TE, H, nTE, _, _ = out.to_python()
return nTE
