4,269 research outputs found
PySpike - A Python library for analyzing spike train synchrony
Understanding how the brain functions is one of the biggest challenges of our
time. The analysis of experimentally recorded neural firing patterns (spike
trains) plays a crucial role in addressing this problem. Here, the PySpike
library is introduced, a Python package for spike train analysis providing
parameter-free and time-scale independent measures of spike train synchrony. It
allows to compute similarity and dissimilarity profiles, averaged values and
distance matrices. Although mainly focusing on neuroscience, PySpike can also
be applied in other contexts like climate research or social sciences. The
package is available as Open Source on Github and PyPI.Comment: 7 pages, 6 figure
A guide to time-resolved and parameter-free measures of spike train synchrony
Measures of spike train synchrony have proven a valuable tool in both
experimental and computational neuroscience. Particularly useful are
time-resolved methods such as the ISI- and the SPIKE-distance, which have
already been applied in various bivariate and multivariate contexts. Recently,
SPIKE-Synchronization was proposed as another time-resolved synchronization
measure. It is based on Event-Synchronization and has a very intuitive
interpretation. Here, we present a detailed analysis of the mathematical
properties of these three synchronization measures. For example, we were able
to obtain analytic expressions for the expectation values of the ISI-distance
and SPIKE-Synchronization for Poisson spike trains. For the SPIKE-distance we
present an empirical formula deduced from numerical evaluations. These
expectation values are crucial for interpreting the synchronization of spike
trains measured in experiments or numerical simulations, as they represent the
point of reference for fully randomized spike trains.Comment: 8 pages, 4 figure
Measuring spike train synchrony
Estimating the degree of synchrony or reliability between two or more spike
trains is a frequent task in both experimental and computational neuroscience.
In recent years, many different methods have been proposed that typically
compare the timing of spikes on a certain time scale to be fixed beforehand.
Here, we propose the ISI-distance, a simple complementary approach that
extracts information from the interspike intervals by evaluating the ratio of
the instantaneous frequencies. The method is parameter free, time scale
independent and easy to visualize as illustrated by an application to real
neuronal spike trains obtained in vitro from rat slices. In a comparison with
existing approaches on spike trains extracted from a simulated Hindemarsh-Rose
network, the ISI-distance performs as well as the best time-scale-optimized
measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor
modifications, added link to webpage that includes the Matlab Source Code for
the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html
Which spike train distance is most suitable for distinguishing rate and temporal coding?
Background: It is commonly assumed in neuronal coding that repeated
presentations of a stimulus to a coding neuron elicit similar responses. One
common way to assess similarity are spike train distances. These can be divided
into spike-resolved, such as the Victor-Purpura and the van Rossum distance,
and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance.
New Method: We use independent steady-rate Poisson processes as surrogates
for spike trains with fixed rate and no timing information to address two basic
questions: How does the sensitivity of the different spike train distances to
temporal coding depend on the rates of the two processes and how do the
distances deal with very low rates?
Results: Spike-resolved distances always contain rate information even for
parameters indicating time coding. This is an issue for reasonably high rates
but beneficial for very low rates. In contrast, the operational range for
detecting time coding of time-resolved distances is superior at normal rates,
but these measures produce artefacts at very low rates. The RI-SPIKE-distance
is the only measure that is sensitive to timing information only.
Comparison with Existing Methods: While our results on rate-dependent
expectation values for the spike-resolved distances agree with
\citet{Chicharro11}, we here go one step further and specifically investigate
applicability for very low rates.
Conclusions: The most appropriate measure depends on the rates of the data
being analysed. Accordingly, we summarize our results in one table that allows
an easy selection of the preferred measure for any kind of data.Comment: 14 pages, 6 Figures, 1 Tabl
Bootstrap testing for cross-correlation under low firing activity
A new cross-correlation synchrony index for neural activity is proposed. The
index is based on the integration of the kernel estimation of the
cross-correlation function. It is used to test for the dynamic synchronization
levels of spontaneous neural activity under two induced brain states:
sleep-like and awake-like. Two bootstrap resampling plans are proposed to
approximate the distribution of the test statistics. The results of the first
bootstrap method indicate that it is useful to discern significant differences
in the synchronization dynamics of brain states characterized by a neural
activity with low firing rate. The second bootstrap method is useful to unveil
subtle differences in the synchronization levels of the awake-like state,
depending on the activation pathway.Comment: 22 pages, 7 figure
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