4,254 research outputs found
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
Classification of high dimensional data using LASSO ensembles
Urda, D., Franco, L. and Jerez, J.M. (2017). Classification of high dimensional data using LASSO ensembles. Proceedings IEEE SSCI'17, Symposium Series on Computational Intelligence, Honolulu, Hawaii, U.S.A. (2017). ISBN: 978-1-5386-2726-6The estimation of multivariable predictors with good performance in high dimensional settings is a crucial task in biomedical contexts. Usually, solutions based on the application
of a single machine learning model are provided while the use of ensemble methods is often overlooked within this area despite
the well-known benefits that these methods provide in terms of predictive performance. In this paper, four ensemble approaches are described using LASSO base learners to predict the vital status of a patient from RNA-Seq gene expression data. The results of the analysis carried out in a public breast invasive cancer (BRCA) dataset shows that the ensemble approaches outperform statistically significant the standard LASSO model
considered as baseline case. We also perform an analysis of the computational costs involved for each of the approaches,
providing different usage recommendations according to the available computational power.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tec
Compression and Classification Methods for Galaxy Spectra in Large Redshift Surveys
Methods for compression and classification of galaxy spectra, which are
useful for large galaxy redshift surveys (such as the SDSS, 2dF, 6dF and
VIRMOS), are reviewed. In particular, we describe and contrast three methods:
(i) Principal Component Analysis, (ii) Information Bottleneck, and (iii) Fisher
Matrix. We show applications to 2dF galaxy spectra and to mock semi-analytic
spectra, and we discuss how these methods can be used to study physical
processes of galaxy formation, clustering and galaxy biasing in the new large
redshift surveys.Comment: Review talk, proceedings of MPA/MPE/ESO Conference "Mining the Sky",
2000, Garching, Germany; 20 pages, 5 figure
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