From 7eea5fea995e432ae7cd6192be7b014ed3b7b2a0 Mon Sep 17 00:00:00 2001 From: zottelsheep <31415206+zottelsheep@users.noreply.github.com> Date: Sun, 4 Jun 2023 17:28:31 +0200 Subject: [PATCH] docs: Fix typos Co-authored-by: Moritz Kern <92092328+Moritz-Alexander-Kern@users.noreply.github.com> --- doc/bib/elephant.bib | 2 +- .../total_spiking_probability_edges.py | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/bib/elephant.bib b/doc/bib/elephant.bib index d9b3d8957..c5b6c8d8a 100644 --- a/doc/bib/elephant.bib +++ b/doc/bib/elephant.bib @@ -441,7 +441,7 @@ @article{de_blasi19_169 doi = {10.1016/j.jneumeth.2018.11.013}, language = {en}, journal = {Journal of Neuroscience Methods}, - author = {De Blasi, Stefano and Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, + author = {{De Blasi}, Stefano and Ciba, Manuel and Bahmer, Andreas and Thielemann, Christiane}, month = jan, year = {2019}, pages = {169--181}, diff --git a/elephant/functional_connectivity_src/total_spiking_probability_edges.py b/elephant/functional_connectivity_src/total_spiking_probability_edges.py index e51d3cb7d..1bc342212 100644 --- a/elephant/functional_connectivity_src/total_spiking_probability_edges.py +++ b/elephant/functional_connectivity_src/total_spiking_probability_edges.py @@ -16,9 +16,9 @@ def total_spiking_probability_edges( ): r""" Use total spiking probability edges (TSPE) to estimate - the funcitional connectivity and delay-times of a neural-network. + the functional connectivity and delay-times of a neural-network. - This algorithm uses a normalized crosscorrelation between pairs of + This algorithm uses a normalized cross correlation between pairs of spiketrains at different delay-times to get a cross-correlogram. Afterwards a series of convolutions with multiple edge-filters on the cross-correlogram are preformed, in order to estimate the @@ -30,7 +30,7 @@ def total_spiking_probability_edges( *Background:* - - On an excitatory connection the spikerate increases and decreases again due to the refractory period which results in lokal maxima in the cross-correlogram followed by downwards slope + - On an excitatory connection the spikerate increases and decreases again due to the refractory period which results in local maxima in the cross-correlogram followed by downwards slope - On an inhibitory connection the spikerate decreases and after refractory period, increases again which results in lokal minima surrounded by high values in the cross-correlogram. @@ -64,10 +64,10 @@ def total_spiking_probability_edges( Parameters ---------- - spiketrains : (N, ) elephant.conversion.BinnedSpikeTrain + spike_trains : (N, ) elephant.conversion.BinnedSpikeTrain A binned spike train containing all neurons for connectivity estimation surrounding_window_sizes : List[int], default = [3, 4, 5, 6, 7, 8] - Array of window-sizes for the surroundig area of the point of interesst. + Array of window-sizes for the surrounding area of the point of interest. observed_window_sizes : List[int], default = [2, 3, 4, 5, 6] Array of window-sizes for the observed area crossover_window_sizes : List[int], default = [0]