57,550 research outputs found
Characterizing Width Uniformity by Wave Propagation
This work describes a novel image analysis approach to characterize the
uniformity of objects in agglomerates by using the propagation of normal
wavefronts. The problem of width uniformity is discussed and its importance for
the characterization of composite structures normally found in physics and
biology highlighted. The methodology involves identifying each cluster (i.e.
connected component) of interest, which can correspond to objects or voids, and
estimating the respective medial axes by using a recently proposed wavefront
propagation approach, which is briefly reviewed. The distance values along such
axes are identified and their mean and standard deviation values obtained. As
illustrated with respect to synthetic and real objects (in vitro cultures of
neuronal cells), the combined use of these two features provide a powerful
description of the uniformity of the separation between the objects, presenting
potential for several applications in material sciences and biology.Comment: 14 pages, 23 figures, 1 table, 1 referenc
Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling
This paper investigates the dependence of synchronization transitions of
bursting oscillations on the information transmission delay over scale-free
neuronal networks with attractive and repulsive coupling. It is shown that for
both types of coupling, the delay always plays a subtle role in either
promoting or impairing synchronization. In particular, depending on the
inherent oscillation period of individual neurons, regions of irregular and
regular propagating excitatory fronts appear intermittently as the delay
increases. These delay-induced synchronization transitions are manifested as
well-expressed minima in the measure for spatiotemporal synchrony. For
attractive coupling, the minima appear at every integer multiple of the average
oscillation period, while for the repulsive coupling, they appear at every odd
multiple of the half of the average oscillation period. The obtained results
are robust to the variations of the dynamics of individual neurons, the system
size, and the neuronal firing type. Hence, they can be used to characterize
attractively or repulsively coupled scale-free neuronal networks with delays.Comment: 15 pages, 9 figures; accepted for publication in PLoS ONE [related
work available at http://arxiv.org/abs/0907.4961 and
http://www.matjazperc.com/
Expression cartography of human tissues using self organizing maps
Background: The availability of parallel, high-throughput microarray and sequencing experiments poses a challenge how to best arrange and to analyze the obtained heap of multidimensional data in a concerted way. Self organizing maps (SOM), a machine learning method, enables the parallel sample- and gene-centered view on the data combined with strong visualization and second-level analysis capabilities. The paper addresses aspects of the method with practical impact in the context of expression analysis of complex data sets.
Results: The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten thousands of genes to a few thousands of metagenes where each metagene acts as representative of a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering provide a better signal-to-noise ratio and a better representativeness of the method if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues into essentially three clusters containing nervous, immune system and the remaining tissues. 
Conclusions: The global view on the behavior of a few well-defined modules of correlated and differentially expressed genes is more intuitive and more informative than the separate discovery of the expression levels of hundreds or thousands of individual genes. The metagene approach is less sensitive to a priori selection of genes. It can detect a coordinated expression pattern whose components would not pass single-gene significance thresholds and it is able to extract context-dependent patterns of gene expression in complex data sets.

Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
This paper describes the R package crqa to perform cross-recurrence
quantification analysis of two time series of either a categorical or
continuous nature. Streams of behavioral information, from eye movements to
linguistic elements, unfold over time. When two people interact, such as in
conversation, they often adapt to each other, leading these behavioral levels
to exhibit recurrent states. In dialogue, for example, interlocutors adapt to
each other by exchanging interactive cues: smiles, nods, gestures, choice of
words, and so on. In order for us to capture closely the goings-on of dynamic
interaction, and uncover the extent of coupling between two individuals, we
need to quantify how much recurrence is taking place at these levels. Methods
available in crqa would allow researchers in cognitive science to pose such
questions as how much are two people recurrent at some level of analysis, what
is the characteristic lag time for one person to maximally match another, or
whether one person is leading another. First, we set the theoretical ground to
understand the difference between 'correlation' and 'co-visitation' when
comparing two time series, using an aggregative or cross-recurrence approach.
Then, we describe more formally the principles of cross-recurrence, and show
with the current package how to carry out analyses applying them. We end the
paper by comparing computational efficiency, and results' consistency, of crqa
R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect
comparability between the two libraries on both levels
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
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