Release 0.10.0
Documentation
The documentation is revised and restructured by categories (#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 support
Analysis of Sequences of Synchronous EvenTs has become the first module in Elephant that supports CUDA and OpenCL (#351, #404, #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 (#400) and reimplemented the continuous time version (#397)
- Improved memory efficiency of creating a BinnedSpikeTrain (#395)
New functionality and features
- Synchrofact detection (#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_difference
functions (https://github.com/NeuralEnsemble/elephant/pull/385/files) - BinnedSpikeTrain:
- added
to_spike_trains
andtime_slice
functions (#390). Now you can slice a binned spike train asbst[:, i:j]
orbst.time_slice(t_start, t_stop)
. Also, withto_spike_trains
function, you can generate a realization of spike trains that maps to the same BinnedSpikeTrain object when binned. - optional CSC format (#402)
- the
copy
parameter (False by default) in thebinarize
function makes a shallow copy, if set to True, of the output BinnedSpikeTrain object (#402)
- added
- Granger causality tutorial notebook (#393)
- Unitary Event Analysis support multiple pattern hashes (#387)