Release Notes

Elephant 0.11.1 release notes

Bug fixes

  • Fix installation on macOS (#472)

Documentation

  • Added example to asset.discretise_spiketimes docstring (#468)

Optimizations

  • Performance improvement of Spike Time Tiling Coefficient (STTC) (#438)

Other changes

  • Continuous Integration (CI): added two workflows for macOS (#474)
  • Fixed failing unit test asset on macOS (#474)

Selected dependency changes

  • scipy >=1.5.4 (#473)

Elephant 0.11.0 release notes

Breaking changes

  • For current source density measures electrode coordinates can no longer be supplied via a RecordingChannelGroup object as it is no longer supported in Neo v0.10.0 (#447)

New functionality and features

  • Redesigned elephant.spike_train_generation module using classes (old API is retained for compatibility) (#416)
  • Added function to calculate the multitaper power spectral density estimate in elephant.spectral (#417)
  • Added a boundary correction for the firing rate estimator elephant.statistics.instantaneous_rate with Gaussian kernels (#414)
  • Function to discretise spiketimes for a given spiketrain in elephant.conversion (#454)
  • Support for the new SpikeTrainList object of Neo (#447)

Bug fixes

  • Issue with unit scaling in BinnedSpikeTrain (#425)
  • Changed BinnedSpikeTrain to support quantities<0.12.4 (#418)
  • Fix FloatingPointError in ICSD (#421)
  • t_start information was lost while transposing LFP for current_source_density module (#432)
  • Fix neo_tools unit tests to work with Neo 0.10.0+ (#446)
  • Fixed various issues with consistency of bin boundaries of instantaneous rates (#453)

Documentation

  • Update tutorials ASSET and UE tutorial and datasets to use nixio >=1.5.0 (#441)
  • Updated spade tutorial to work with viziphant 0.2.0 (#444)
  • Fixed figures in the Granger causality tutorial (#434)
  • Add DOIs to documentation (#456)
  • Fixed random seed selection in some tutorials (#430)

Optimizations

  • Highly optimized run-time of the SPADE analysis (#419)
  • More efficient storage of spike complexities by the elephant.statistics.Complexity class (#412)
  • Updated elephant.signal_processing.zscore function for in-place operations (#440)

Other changes

  • Continuous Integration (CI) was moved to github actions (#451)
  • Change test framework from Nose to pytest (#413)
  • Added DOI with zenodo (#445)
  • Versioning for associated elephant-data repository for example datasets introduced (#463)

Selected dependency changes

  • nixio >= 1.5.0
  • neo >= 0.10.0
  • python >= 3.7

Elephant 0.10.0 release notes

Documentation

The documentation is revised and restructured by categories (https://github.com/NeuralEnsemble/elephant/pull/386) to simplify navigation on readthedocs and improve user experience. All citations used in Elephant are stored in a single [BibTex file](https://github.com/NeuralEnsemble/elephant/blob/master/doc/bib/elephant.bib).

Optimizations

CUDA and OpenCL support

[Analysis of Sequences of Synchronous EvenTs](https://elephant.readthedocs.io/en/latest/reference/asset.html) has become the first module in Elephant that supports CUDA and OpenCL (https://github.com/NeuralEnsemble/elephant/pull/351, https://github.com/NeuralEnsemble/elephant/pull/404, https://github.com/NeuralEnsemble/elephant/pull/399). Whether you have an Nvidia GPU or just run the analysis on a laptop with a built-in Intel graphics card, the speed-up is X100 and X1000 compared to a single CPU core. The computations are optimized to a degree that you can analyse and look for spike patterns in real data in several minutes of compute time on a laptop. The installation instructions are described in the [install](https://elephant.readthedocs.io/en/latest/install.html) section.

Other optimizations

New functionality and features

Bug fixes

Elephant 0.9.0 release notes

This release is titled to accompany the [2nd Elephant User Workshop](https://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-trainings/2nd-elephant-user-workshop/)

Viziphant

Meet Viziphant, the visualization of Elephant analysis methods, at https://viziphant.readthedocs.io/en/latest/. This package provides support to easily plot and visualize the output of Elephant functions in a few lines of code.

Provenance tracking

Provenance is becoming a separate direction in Elephant. Many things are still to come, and we started with annotating time_histogram, instantaneous_rate and cross_correlation_histogram outputs to carry the information about the parameters these functions used. This allowed Viziphant, the visualization of Elephant analyses, to look for the .annotations dictionary of the output of these function to “understand” how the object has been generated and label the plot axes accordingly.

New functionality and features

Python 3 only

Optimization

  • You have been asking for direct numpy support for years. Added _t_start, _t_stop, and _bin_size attributes of BinnedSpikeTrain are guaranteed to be of the same units and hence are unitless (https://github.com/NeuralEnsemble/elephant/pull/378). It doesn’t mean though that you need to care about units on your own: t_start, t_stop, and bin_size properties are still quantities with units. The .rescale() method of a BinnedSpikeTrain rescales the internal units to new ones in-place. The following Elephant functions are optimized with unitless BinnedSpikeTrain: - cross_correlation_histogram - bin_shuffling (one of the surrogate methods) - spike_train_timescale
  • X4 faster binning and overall BinnedSpikeTrain object creation (https://github.com/NeuralEnsemble/elephant/pull/368).
  • instantaneous_rate function is vectorized to work with a list of spike train trials rather than computing them in a loop (previously, for spiketrain in spiketrains; do compute instantaneous_rate(spiketrain); done), which brought X25 speedup (https://github.com/NeuralEnsemble/elephant/pull/362; thanks to @gyyang for the idea and original implementation).
  • Memory-efficient zscore function (https://github.com/NeuralEnsemble/elephant/pull/372).
  • Don’t sort the input array in ISI function (https://github.com/NeuralEnsemble/elephant/pull/371), which reduces function algorithmic time complexity from O(N logN) to linear O(N). Now, when the input time array is not sorted, a warning is shown.
  • Vectorized Current Source Density generate_lfp function (https://github.com/NeuralEnsemble/elephant/pull/358).

Breaking changes

  • mpi4py package is removed from the extra requirements to allow pip install elephant[extras] on machines without MPI installed system-wide. Refer to [MPI support](https://elephant.readthedocs.io/en/latest/install.html#mpi-support) installation page in elephant.
  • BinnedSpikeTrain (https://github.com/NeuralEnsemble/elephant/pull/368, https://github.com/NeuralEnsemble/elephant/pull/377): - previously, when t_start/stop, if set manually, was outside of the shared time interval, only the shared [t_start_shared=max(t_start), t_stop_shared=min(t_stop)] interval was implicitly considered without any warnings. Now an error is thrown with a description on how to fix it. - removed lst_input, input_spiketrains, matrix_columns, matrix_rows (in favor of the new attribute - shape), tolerance, is_spiketrain, is_binned attributes from BinnedSpikeTrain class. Part of them are confusing (e.g., is_binned was just the opposite of is_spiketrain, but one can erroneously think that it’s data is clipped to 0 and 1), and part of them - lst_input, input_spiketrains input data - should not have been saved as attributes of an object in the first place because the input spike trains are not used after the sparse matrix is created. - now the users can directly access .sparse_matrix attribute of BinnedSpikeTrain to do efficient (yet unsafe in general) operations. For this reason, to_sparse_array() function, which does not make a copy, as one could think of, is deprecated.
  • instantaneous_rate function (https://github.com/NeuralEnsemble/elephant/pull/362): - in case of multiple input spike trains, the output of the instantaneous rate function is (always) a 2D matrix of shape (time, len(spiketrains)) instead of a pseudo 1D array (previous behavior) of shape (time, 1) that contained the instantaneous rate summed across input spike trains; - in case of multiple input spike trains, the user needs to manually provide the input kernel instead of auto, which is set by default, for the reason that it’s currently not clear how to estimate the common kernel for a set of spike trains. If you have an idea how to do this, we`d appreciate if you let us know by [getting in touch with us](https://elephant.readthedocs.io/en/latest/get_in_touch.html).

Other changes

Bug fixes

Elephant 0.8.0 release notes

New features

New tutorials

Optimization

Python 2.7 and 3.5 deprecation

Python 2.7 and 3.5 are deprecated and will not be maintained by the end of 2020. Switch to Python 3.6+.

Breaking changes

Elephant 0.7.0 release notes

Breaking changes

gpfa = GPFA(bin_size=20*pq.ms, x_dim=8)
results = gpfa.fit_transform(spiketrains, returned_data=['xorth', 'xsm'])

New tutorials

Deprecations

  • Python 2.7 support will be dropped on Dec 31, 2020. Please switch to Python 3.6, 3.7, or 3.8.
  • [spike train generation] homogeneous_poisson_process_with_refr_period(), introduced in v0.6.4, is deprecated and will be deleted in v0.8.0. Use homogeneous_poisson_process(refractory_period=…) instead.
  • [pandas bridge] pandas_bridge module is deprecated and will be deleted in v0.8.0.

New features

Bug fixes

Performance

Elephant 0.6.4 release notes

This release has been made for the “1st Elephant User Workshop” (https://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-trainings/1st-elephant-user-workshop-accelerate-structured-and-reproducibl).

Main features

  • neo v0.8.0 compatible

New modules

Bug fixes

Improvements

Elephant 0.6.3 release notes

July 22nd 2019

The release v0.6.3 is mostly about improving maintenance.

New functions

Elephant 0.6.2 release notes

April 23rd 2019

New functions

  • signal_processing module
    • New functions to calculate the area under a time series and the derivative of a time series.

Other changes

  • Added support to initialize binned spike train representations with a matrix
  • Multiple bug fixes

Elephant 0.6.1 release notes

April 1st 2019

New functions

  • signal_processing module
    • New function to calculate the cross-correlation function for analog signals.
  • spade module
    • Spatio-temporal spike pattern detection now includes the option to assess significance also based on time-lags of patterns, in addition to patterns size and frequency (referred to as 3D pattern spectrum).

Other changes

  • This release fixes a number of compatibility issues in relation to API breaking changes in the Neo library.
  • Fixed error in STTC calculation (spike time tiling coefficient)
  • Minor bug fixes

Elephant 0.6.0 release notes

October 12th 2018

New functions

  • cell_assembly_detection module
    • New function to detect higher-order correlation structures such as patterns in parallel spike trains based on Russo et al, 2017.
  • wavelet_transform() function in signal_prosessing.py module
    • Function for computing wavelet transform of a given time series based on Le van Quyen et al. (2001)

Other changes

  • Switched to multiple requirements.txt files which are directly read into the setup.py
  • instantaneous_rate() accepts now list of spiketrains
  • Minor bug fixes

Elephant 0.5.0 release notes

April 4nd 2018

New functions

  • change_point_detection module:
    • New function to detect changes in the firing rate
  • spike_train_correlation module:
    • New function to calculate the spike time tiling coefficient
  • phase_analysis module:
    • New function to extract spike-triggered phases of an AnalogSignal
  • unitary_event_analysis module:
    • Added new unit test to the UE function to verify the method based on data of a recent [Re]Science publication

Other changes

  • Minor bug fixes

Elephant 0.4.3 release notes

March 2nd 2018

Other changes

  • Bug fixes in spade module:
    • Fixed an incompatibility with the latest version of an external library

Elephant 0.4.2 release notes

March 1st 2018

New functions

  • spike_train_generation module:
    • inhomogeneous_poisson() function
  • Modules for Spatio Temporal Pattern Detection (SPADE) spade_src:
    • Module SPADE: spade.py
  • Module statistics.py:
    • Added CV2 (coefficient of variation for non-stationary time series)
  • Module spike_train_correlation.py:
    • Added normalization in cross-correlation histogram() (CCH)

Other changes

  • Adapted the setup.py to automatically install the spade modules including the compiled C files fim.so
  • Included testing environment for MPI in travis.yml
  • Changed function arguments in current_source_density.py to neo.AnalogSignal instead list of neo.AnalogSignal objects
  • Fixes to travis and setup configuration files
  • Fixed bug in ISI function isi(), statistics.py module
  • Fixed bug in dither_spikes(), spike_train_surrogates.py
  • Minor bug fixes

Elephant 0.4.1 release notes

March 23rd 2017

Other changes

  • Fix in setup.py to correctly import the current source density module

Elephant 0.4.0 release notes

March 22nd 2017

New functions

  • spike_train_generation module:
    • peak detection: peak_detection()
  • Modules for Current Source Density: current_source_density_src
    • Module Current Source Density: KCSD.py
    • Module for Inverse Current Source Density: icsd.py

API changes

  • Interoperability between Neo 0.5.0 and Elephant

Other changes

  • Fixes to travis and setup configuration files.
  • Minor bug fixes.
  • Added module six for Python 2.7 backwards compatibility

Elephant 0.3.0 release notes

April 12st 2016

New functions

  • spike_train_correlation module:
    • cross correlation histogram: cross_correlation_histogram()
  • spike_train_generation module:
    • single interaction process (SIP): single_interaction_process()
    • compound Poisson process (CPP): compound_poisson_process()
  • signal_processing module:
    • analytic signal: hilbert()
  • sta module:
    • spike field coherence: spike_field_coherence()
  • Module to represent kernels: kernels module
  • Spike train metrics / dissimilarity / synchrony measures: spike_train_dissimilarity module
  • Unitary Event (UE) analysis: unitary_event_analysis module
  • Analysis of Sequences of Synchronous EvenTs (ASSET): asset module

API changes

  • Function instantaneous_rate() now uses kernels as objects defined in the kernels module. The previous implementation of the function using the make_kernel() function is deprecated, but still temporarily available as oldfct_instantaneous_rate().

Other changes

  • Fixes to travis and readthedocs configuration files.

Elephant 0.2.1 release notes

February 18th 2016

Other changes

Minor bug fixes.

Elephant 0.2.0 release notes

September 22nd 2015

New functions

  • Added covariance function covariance() in the spike_train_correlation module
  • Added complexity pdf complexity_pdf() in the statistics module
  • Added spike train extraction from analog signals via threshold detection the in spike_train_generation module
  • Added coherence() function for analog signals in the spectral module
  • Added Cumulant Based Inference for higher-order of Correlation (CuBIC) in the cubic module for correlation analysis of parallel recorded spike trains

API changes

  • Optimized kernel bandwidth in rate_estimation function: Calculates the optimized kernel width when the paramter kernel width is specified as auto

Other changes

  • Optimized creation of sparse matrices: The creation speed of the sparse matrix inside the BinnedSpikeTrain class is optimized
  • Added Izhikevich neuron simulator in the make_spike_extraction_test_data module
  • Minor improvements to the test and continous integration infrastructure