NeuralEnsemble/elephant: Release 0.10.0
Authors/Creators
- Danylo Ulianych1
- Andrew Davison
- Alper2
- Michael Denker3
- Pietro Quaglio4
- pbouss
- Junji Ito5
- Richard C Gerkin6
- Detlef Holstein
- Daniel Müller-Komorowska
- Todd
- Alexander Kleinjohann7
- Regimantas Jurkus
- Aitor Morales-Gregorio
- etorre
- Julia Sprenger8
- Paulina Dąbrowska
- Robin Gutzen7
- Alessandra Stella
- Espen Hagen
- Vahidrostami
- Alexander van Meegen
- EmanueleLucrezia
- Robert Pröpper
- Simon Essink
- Bartosz Telenczuk9
- Björn Müller
- Broxy7
- Chaitanya Chintaluri10
- FMendezSlc
- 1. KyivAIGroup
- 2. Research Center Juelich
- 3. Institute of Neuroscience and Medicine (INM-10/INM-6), Forschungszentrum Jülich
- 4. INM6, Forschungzentrum Juelich
- 5. INM-6 and IAS-6, Jülich Research Centre and JARA
- 6. Ozymandian Industries
- 7. @INM-6
- 8. Institut de Neurosciences de la Timone (INT)
- 9. freelancer
- 10. University of Oxford
Description
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.
Optimizations CUDA and OpenCL supportAnalysis of Sequences of Synchronous EvenTs 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 section.
Other optimizations- Surrogates: sped up bin shuffling (https://github.com/NeuralEnsemble/elephant/pull/400) and reimplemented the continuous time version (https://github.com/NeuralEnsemble/elephant/pull/397)
- Improved memory efficiency of creating a BinnedSpikeTrain (https://github.com/NeuralEnsemble/elephant/pull/395)
- Synchrofact detection (https://github.com/NeuralEnsemble/elephant/pull/322) is a method to detect highly synchronous spikes (at the level of sampling rate precision with an option to extend this to jittered synchrony) and annotate or optionally remove them.
- Added
phase_locking_value,mean_phase_vector, andphase_differencefunctions (https://github.com/NeuralEnsemble/elephant/pull/385/files) - BinnedSpikeTrain:
- added
to_spike_trainsandtime_slicefunctions (https://github.com/NeuralEnsemble/elephant/pull/390). Now you can slice a binned spike train asbst[:, i:j]orbst.time_slice(t_start, t_stop). Also, withto_spike_trainsfunction, you can generate a realization of spike trains that maps to the same BinnedSpikeTrain object when binned. - optional CSC format (https://github.com/NeuralEnsemble/elephant/pull/402)
- the
copyparameter (False by default) in thebinarizefunction makes a shallow copy, if set to True, of the output BinnedSpikeTrain object (https://github.com/NeuralEnsemble/elephant/pull/402)
- added
- Granger causality tutorial notebook (https://github.com/NeuralEnsemble/elephant/pull/393)
- Unitary Event Analysis support multiple pattern hashes (https://github.com/NeuralEnsemble/elephant/pull/387)
- Account for unidirectional spiketrain->segment links in synchrofact deletion (https://github.com/NeuralEnsemble/elephant/pull/398)
- Joint-ISI dithering: fixed a bug regarding first ISI bin (https://github.com/NeuralEnsemble/elephant/pull/396)
- Fix LvR values from being off when units are in seconds (https://github.com/NeuralEnsemble/elephant/pull/389)
Files
NeuralEnsemble/elephant-v0.10.0.zip
Files
(2.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:5203b20a769fb7020339f56dec9e1d9a
|
2.1 MB | Preview Download |
Additional details
Related works
- Is supplement to
- https://github.com/NeuralEnsemble/elephant/tree/v0.10.0 (URL)