18,950 research outputs found
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
Group Analysis of Self-organizing Maps based on Functional MRI using Restricted Frechet Means
Studies of functional MRI data are increasingly concerned with the estimation
of differences in spatio-temporal networks across groups of subjects or
experimental conditions. Unsupervised clustering and independent component
analysis (ICA) have been used to identify such spatio-temporal networks. While
these approaches have been useful for estimating these networks at the
subject-level, comparisons over groups or experimental conditions require
further methodological development. In this paper, we tackle this problem by
showing how self-organizing maps (SOMs) can be compared within a Frechean
inferential framework. Here, we summarize the mean SOM in each group as a
Frechet mean with respect to a metric on the space of SOMs. We consider the use
of different metrics, and introduce two extensions of the classical sum of
minimum distance (SMD) between two SOMs, which take into account the
spatio-temporal pattern of the fMRI data. The validity of these methods is
illustrated on synthetic data. Through these simulations, we show that the
three metrics of interest behave as expected, in the sense that the ones
capturing temporal, spatial and spatio-temporal aspects of the SOMs are more
likely to reach significance under simulated scenarios characterized by
temporal, spatial and spatio-temporal differences, respectively. In addition, a
re-analysis of a classical experiment on visually-triggered emotions
demonstrates the usefulness of this methodology. In this study, the
multivariate functional patterns typical of the subjects exposed to pleasant
and unpleasant stimuli are found to be more similar than the ones of the
subjects exposed to emotionally neutral stimuli. Taken together, these results
indicate that our proposed methods can cast new light on existing data by
adopting a global analytical perspective on functional MRI paradigms.Comment: 23 pages, 5 figures, 4 tables. Submitted to Neuroimag
Highly comparative feature-based time-series classification
A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time series in terms of their correlation structure, distribution, entropy,
stationarity, scaling properties, and fits to a range of time-series models.
After computing thousands of features for each time series in a training set,
those that are most informative of the class structure are selected using
greedy forward feature selection with a linear classifier. The resulting
feature-based classifiers automatically learn the differences between classes
using a reduced number of time-series properties, and circumvent the need to
calculate distances between time series. Representing time series in this way
results in orders of magnitude of dimensionality reduction, allowing the method
to perform well on very large datasets containing long time series or time
series of different lengths. For many of the datasets studied, classification
performance exceeded that of conventional instance-based classifiers, including
one nearest neighbor classifiers using Euclidean distances and dynamic time
warping and, most importantly, the features selected provide an understanding
of the properties of the dataset, insight that can guide further scientific
investigation
IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY
13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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