237,200 research outputs found
One hundred ways to process time, frequency, rate and scale in the central auditory system: a pattern-recognition meta-analysis
International audienceThe mammalian auditory system extracts features from the acoustic environment based on the responses of spatially distributed sets of neurons in the subcortical and cortical auditory structures. The characteristic responses of these neurons (linearly approximated by their spectro-temporal receptive fields, or STRFs) suggest that auditory representations are formed, as early as in the inferior colliculi, on the basis of a time, frequency, rate (temporal modulations) and scale (spectral modulations) analysis of sound. However, how these four dimensions are integrated and processed in subsequent neural networks remains unclear. In this work, we present a new methodology to generate computational insights into the functional organization of such processes. We first propose a systematic framework to explore more than a hundred different computational strategies proposed in the literature to process the output of a generic STRF model. We then evaluate these strategies on their ability to compute perceptual distances between pairs of environmental sounds. Finally, we conduct a meta-analysis of the dataset of all these algorithms' accuracies to examine whether certain combinations of dimensions and certain ways to treat such dimensions are, on the whole, more computationally effective than others. We present an application of this methodology to a dataset of ten environmental sound categories, in which the analysis reveals that (1) models are most effective when they organize STRF data into frequency groupings—which is consistent with the known tonotopic organization of receptive fields in auditory structures-, and that (2) models that treat STRF data as time series are no more effective than models that rely only on summary statistics along time—which corroborates recent experimental evidence on texture discrimination by summary statistics
Photometric redshift galaxies as tracers of the filamentary network
Galaxy filaments are the dominant feature in the overall structure of the
cosmic web. The study of the filamentary web is an important aspect in
understanding galaxy evolution and the evolution of matter in the Universe. A
map of the filamentary structure is an adequate probe of the web. We propose
that photometric redshift galaxies are significantly positively associated with
the filamentary structure detected from the spatial distribution of
spectroscopic redshift galaxies. The catalogues of spectroscopic and
photometric galaxies are seen as point-process realisations in a sphere, and
the catalogue of filamentary spines is proposed to be a realisation of a random
set in a sphere. The positive association between these sets was studied using
a bivariate function, which is a summary statistics studying clustering. A
quotient was built to estimate the distance distribution of the filamentary
spine to galaxies in comparison to the distance distribution of the filamentary
spine to random points in dimensional Euclidean space. This measure gives a
physical distance scale to the distances between filamentary spines and the
studied sets of galaxies. The bivariate function shows a statistically
significant clustering effect in between filamentary spines and photometric
redshift galaxies. The quotient confirms the previous result that smaller
distances exist with higher probability between the photometric galaxies and
filaments. The trend of smaller distances between the objects grows stronger at
higher redshift. Additionally, the quotient for photometric galaxies gives
a rough estimate for the filamentary spine width of about ~Mpc. Photometric
redshift galaxies are positively associated with filamentary spines detected
from the spatial distribution of spectroscopic galaxies.Comment: Accepted to Astronomy & Astrophysics. 13 pages and 9 figure
Score, Pseudo-Score and Residual Diagnostics for Spatial Point Process Models
We develop new tools for formal inference and informal model validation in
the analysis of spatial point pattern data. The score test is generalized to a
"pseudo-score" test derived from Besag's pseudo-likelihood, and to a class of
diagnostics based on point process residuals. The results lend theoretical
support to the established practice of using functional summary statistics,
such as Ripley's -function, when testing for complete spatial randomness;
and they provide new tools such as the compensator of the -function for
testing other fitted models. The results also support localization methods such
as the scan statistic and smoothed residual plots. Software for computing the
diagnostics is provided.Comment: Published in at http://dx.doi.org/10.1214/11-STS367 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
On central tendency and dispersion measures for intervals and hypercubes
The uncertainty or the variability of the data may be treated by considering,
rather than a single value for each data, the interval of values in which it
may fall. This paper studies the derivation of basic description statistics for
interval-valued datasets. We propose a geometrical approach in the
determination of summary statistics (central tendency and dispersion measures)
for interval-valued variables
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