Neural Data Analysis#
braintools.analysis collects measures for analysing recorded or simulated neural activity. They are grouped by the kind of data they take: braintools.analysis.spike for spike trains, braintools.analysis.spectral for spectra and coupling of continuous signals, braintools.analysis.field for local field potentials and current source density, braintools.analysis.population for multichannel activity, and braintools.analysis.stats for correlation statistics, resampling and bootstrap confidence intervals. Each measure is checked in these tutorials against a case with a known answer.
These functions used to live in braintools.metric; the old names still work in 0.4 but warn when called and will be removed in 1.0.
What you’ll find here#
Firing rates and population synchrony: rasters,
firing_rate, the chance level of the spike-count coherence \(\kappa\), Golomb’s \(\chi^2\), and functional connectivity (and its dynamics).Spike-train distances and synchrony: Victor-Purpura and van Rossum distances, coincidence measures versus STTC for independent trains, phase locking, and burst synchrony.
LFP analysis: generating an LFP from spikes, Welch spectra and frequency resolution, coherence, spectral entropy, phase-amplitude coupling, phase coherence, and current source density.
The analysis toolbox: interval statistics, PSTHs, correlograms and ISI-/SPIKE-distances on
SpikeTrains; multitaper spectra, Morlet wavelets, band-pass filtering and PLV/PLI/wPLI; PCA, decoding, Granger causality and avalanches; significance tests by spike jitter and phase randomisation, and bootstrap confidence intervals.
For the full list of functions and parameters, see the API reference.