netpyne.analysis.info
Module for analyzing and plotting information theory results
Functions:

Function that calculates the Normalized Transfer Entropy (nTE) between two spike train signals. 

Function that calculates Granger Causality between two spike train signals. 

Function that calculates Granger Causality between two spike train signals. 
 netpyne.analysis.info.nTE(cells1=[], cells2=[], spks1=None, spks2=None, timeRange=None, binSize=20, numShuffle=30)[source]
Function that calculates the Normalized Transfer Entropy (nTE) between two spike train signals.
Transfer entropy is a modelfree statistic that is able to measure the timedirected 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 populationscells2 (list) – Subset of cells from which to obtain spike train 2. Default:
[]
Options: same as for cells1spks1 (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(X2FX2P). Default:
30
Options:<option>
<description of option>
 netpyne.analysis.info.plotGranger(cells1=None, cells2=None, spks1=None, spks2=None, label1=None, label2=None, timeRange=None, binSize=5, testGranger=False, plotFig=True, saveData=None, saveFig=None, showFig=True)[source]
Function that calculates Granger Causality between two spike train signals.
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. Gcausality is based on the simple idea that causes both precede and help predict their effects.
Seth, A. K., Barrett, A. B., & Barnett, L. (2015). Granger Causality Analysis in Neuroscience and Neuroimaging. Journal of Neuroscience, 35(8), 3293–3297. https://doi.org/10.1523/jneurosci.439914.2015
 Parameters
cells1 (list) – Subset of cells from which to obtain spike train 1. Default:
None
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 populationscells2 (list) – Subset of cells from which to obtain spike train 2. Default:
None
Options: same as for cells1spks1 (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
label1 (str) – Label for spike train 1 to use in plot. Default:
None
label2 (str) – Label for spike train 2 to use in plot. Default:
None
timeRange (list [min, max]) – Range of time to calculate nTE in ms. Default:
None
uses the entire simulation time rangebinSize (int) – Bin size used to convert spike times into histogram. Default:
5
testGranger (bool) – Whether to test the Granger calculation. Default:
False
plotFig (bool) – Whether to plot a figure showing Granger Causality Fx2y and Fy2x Default:
True
saveData (bool or str) – Whether and where to save the data used to generate the plot. Default:
False
Options:True
autosaves the data,'/path/filename.ext'
saves to a custom path and filename, valid file extensions are'.pkl'
and'.json'
saveFig (bool or str) – Whether and where to save the figure. Default:
False
Options:True
autosaves the figure,'/path/filename.ext'
saves to a custom path and filename, valid file extensions are'.png'
,'.jpg'
,'.eps'
, and'.tiff'
showFig (bool) – Shows the figure if
True
. Default:True
 netpyne.analysis.info.granger(cells1=None, cells2=None, spks1=None, spks2=None, label1=None, label2=None, timeRange=None, binSize=5, testGranger=False, plotFig=True, saveData=None, saveFig=None, showFig=True)
Function that calculates Granger Causality between two spike train signals.
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. Gcausality is based on the simple idea that causes both precede and help predict their effects.
Seth, A. K., Barrett, A. B., & Barnett, L. (2015). Granger Causality Analysis in Neuroscience and Neuroimaging. Journal of Neuroscience, 35(8), 3293–3297. https://doi.org/10.1523/jneurosci.439914.2015
 Parameters
cells1 (list) – Subset of cells from which to obtain spike train 1. Default:
None
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 populationscells2 (list) – Subset of cells from which to obtain spike train 2. Default:
None
Options: same as for cells1spks1 (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
label1 (str) – Label for spike train 1 to use in plot. Default:
None
label2 (str) – Label for spike train 2 to use in plot. Default:
None
timeRange (list [min, max]) – Range of time to calculate nTE in ms. Default:
None
uses the entire simulation time rangebinSize (int) – Bin size used to convert spike times into histogram. Default:
5
testGranger (bool) – Whether to test the Granger calculation. Default:
False
plotFig (bool) – Whether to plot a figure showing Granger Causality Fx2y and Fy2x Default:
True
saveData (bool or str) – Whether and where to save the data used to generate the plot. Default:
False
Options:True
autosaves the data,'/path/filename.ext'
saves to a custom path and filename, valid file extensions are'.pkl'
and'.json'
saveFig (bool or str) – Whether and where to save the figure. Default:
False
Options:True
autosaves the figure,'/path/filename.ext'
saves to a custom path and filename, valid file extensions are'.png'
,'.jpg'
,'.eps'
, and'.tiff'
showFig (bool) – Shows the figure if
True
. Default:True