55 research outputs found
Predict or classify: The deceptive role of time-locking in brain signal classification
Several experimental studies claim to be able to predict the outcome of
simple decisions from brain signals measured before subjects are aware of their
decision. Often, these studies use multivariate pattern recognition methods
with the underlying assumption that the ability to classify the brain signal is
equivalent to predict the decision itself. Here we show instead that it is
possible to correctly classify a signal even if it does not contain any
predictive information about the decision. We first define a simple stochastic
model that mimics the random decision process between two equivalent
alternatives, and generate a large number of independent trials that contain no
choice-predictive information. The trials are first time-locked to the time
point of the final event and then classified using standard machine-learning
techniques. The resulting classification accuracy is above chance level long
before the time point of time-locking. We then analyze the same trials using
information theory. We demonstrate that the high classification accuracy is a
consequence of time-locking and that its time behavior is simply related to the
large relaxation time of the process. We conclude that when time-locking is a
crucial step in the analysis of neural activity patterns, both the emergence
and the timing of the classification accuracy are affected by structural
properties of the network that generates the signal.Comment: 23 pages, 5 figure
Self Organization of Interacting Polya Urns
We introduce a simple model which shows non-trivial self organized critical
properties. The model describes a system of interacting units, modelled by
Polya urns, subject to perturbations and which occasionally break down. Three
equivalent formulations - stochastic, quenched and deterministic - are shown to
reproduce the same dynamics. Among the novel features of the model are a
non-homogeneous stationary state, the presence of a non-stationary critical
phase and non-trivial exponents even in mean field. We discuss simple
interpretations in term of biological evolution and earthquake dynamics and we
report on extensive numerical simulations in dimensions as well as in
the random neighbors limit.Comment: 4 pages 1 figur
Biodiversity in model ecosystems, II: Species assembly and food web structure
This is the second of two papers dedicated to the relationship between
population models of competition and biodiversity. Here we consider species
assembly models where the population dynamics is kept far from fixed points
through the continuous introduction of new species, and generalize to such
models thecoexistence condition derived for systems at the fixed point. The
ecological overlap between species with shared preys, that we define here,
provides a quantitative measure of the effective interspecies competition and
of the trophic network topology. We obtain distributions of the overlap from
simulations of a new model based both on immigration and speciation, and show
that they are in good agreement with those measured for three large natural
food webs. As discussed in the first paper, rapid environmental fluctuations,
interacting with the condition for coexistence of competing species, limit the
maximal biodiversity that a trophic level can host. This horizontal limitation
to biodiversity is here combined with either dissipation of energy or growth of
fluctuations, which in our model limit the length of food webs in the vertical
direction. These ingredients yield an effective model of food webs that produce
a biodiversity profile with a maximum at an intermediate trophic level, in
agreement with field studies
Biodiversity in model ecosystems, I: Coexistence conditions for competing species
This is the first of two papers where we discuss the limits imposed by
competition to the biodiversity of species communities. In this first paper we
study the coexistence of competing species at the fixed point of population
dynamic equations. For many simple models, this imposes a limit on the width of
the productivity distribution, which is more severe the more diverse the
ecosystem is (Chesson, 1994). Here we review and generalize this analysis,
beyond the ``mean-field''-like approximation of the competition matrix used in
previous works, and extend it to structured food webs. In all cases analysed,
we obtain qualitatively similar relations between biodiversity and competition:
the narrower the productivity distribution is, the more species can stably
coexist. We discuss how this result, considered together with environmental
fluctuations, limits the maximal biodiversity that a trophic level can host
Single-molecule modeling of mRNA degradation by miRNA: Lessons from data
Recent experimental results on the effect of miRNA on the decay of its target
mRNA have been analyzed against a previously hypothesized single molecule
degradation pathway. According to that hypothesis, the silencing complex
(miRISC) first interacts with its target mRNA and then recruits the protein
complexes associated with NOT1 and PAN3 to trigger deadenylation (and
subsequent degradation) of the target mRNA. Our analysis of the experimental
decay patterns allowed us to refine the structure of the degradation pathways
at the single molecule level. Surprisingly, we found that if the previously
hypothesized network was correct, only about 7% of the target mRNA would be
regulated by the miRNA mechanism, which is inconsistent with the available
knowledge. Based on systematic data analysis, we propose the alternative
hypothesis that NOT1 interacts with miRISC before binding to the target mRNA.
Moreover, we show that when miRISC binds alone to the target mRNA, the mRNA is
degraded more slowly, probably through a deadenylation-independent pathway. The
new biochemical pathway we propose both fits the data and paves the way for new
experimental work to identify new interactions.Comment: It contains also the Supplementary Materials as appendix
Global and local depletion of ternary complex limits translational elongation
The translation of genetic information according to the sequence of the mRNA template occurs with high accuracy and fidelity. Critical events in each single step of translation are selection of transfer RNA (tRNA), codon reading and tRNA-regeneration for a new cycle. We developed a model that accurately describes the dynamics of single elongation steps, thus providing a systematic insight into the sensitivity of the mRNA translation rate to dynamic environmental conditions. Alterations in the concentration of the aminoacylated tRNA can transiently stall the ribosomes during translation which results, as suggested by the model, in two outcomes: either stress-induced change in the tRNA availability triggers the premature termination of the translation and ribosomal dissociation, or extensive demand for one tRNA species results in a competition between frameshift to an aberrant open-reading frame and ribosomal drop-off. Using the bacterial Escherichia coli system, we experimentally draw parallels between these two possible mechanisms
Transient Phenomena in Gene Expression after Induction of Transcription
When transcription of a gene is induced by a stimulus, the number of its mRNA molecules changes with time. Here we discuss how this time evolution depends on the shape of the mRNA lifetime distribution. Analysis of the statistical properties of this change reveals transient effects on polysomes, ribosomal profiles, and rate of protein synthesis. Our studies reveal that transient phenomena in gene expression strongly depend on the specific form of the mRNA lifetime distribution
The shape of ecological networks
We study the statistics of ecosystems with a variable number of co-evolving
species. The species interact in two ways: by prey-predator relationships and
by direct competition with similar kinds. The interaction coefficients change
slowly through successful adaptations and speciations. We treat them as
quenched random variables. These interactions determine long-term topological
features of the species network, which are found to agree with those of
biological systems.Comment: 4 pages, 2 figure
Self-Organized Criticality Driven by Deterministic Rules
We have investigated the essential ingredients allowing a system to show Self
Organized Criticality (SOC) in its collective behavior. Using the Bak-Sneppen
model of biological evolution as our paradigm, we show that the random
microscopic rules of update can be effectively substituted with a chaotic map
without changing the universality class. Using periodic maps SOC is preserved,
but in a different universality class, as long as the spectrum of frequencies
is broad enough.Comment: 4 pages, RevTex (tar.gz), 4 eps-figures include
Self-organized criticality in deterministic systems with disorder
Using the Bak-Sneppen model of biological evolution as our paradigm, we
investigate in which cases noise can be substituted with a deterministic signal
without destroying Self-Organized Criticality (SOC). If the deterministic
signal is chaotic the universality class is preserved; some non-universal
features, such as the threshold, depend on the time correlation of the signal.
We also show that, if the signal introduced is periodic, SOC is preserved but
in a different universality class, as long as the spectrum of frequencies is
broad enough.Comment: RevTex, 8 pages, 8 figure
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