826 research outputs found
Maladaptation and the paradox of robustness in evolution
Get PDFBackground. Organisms use a variety of mechanisms to protect themselves
against perturbations. For example, repair mechanisms fix damage, feedback
loops keep homeostatic systems at their setpoints, and biochemical filters
distinguish signal from noise. Such buffering mechanisms are often discussed in
terms of robustness, which may be measured by reduced sensitivity of
performance to perturbations. Methodology/Principal Findings. I use a
mathematical model to analyze the evolutionary dynamics of robustness in order
to understand aspects of organismal design by natural selection. I focus on two
characters: one character performs an adaptive task; the other character
buffers the performance of the first character against perturbations. Increased
perturbations favor enhanced buffering and robustness, which in turn decreases
sensitivity and reduces the intensity of natural selection on the adaptive
character. Reduced selective pressure on the adaptive character often leads to
a less costly, lower performance trait. Conclusions/Significance. The paradox
of robustness arises from evolutionary dynamics: enhanced robustness causes an
evolutionary reduction in the adaptive performance of the target character,
leading to a degree of maladaptation compared to what could be achieved by
natural selection in the absence of robustness mechanisms. Over evolutionary
time, buffering traits may become layered on top of each other, while the
underlying adaptive traits become replaced by cheaper, lower performance
components. The paradox of robustness has widespread implications for
understanding organismal design
Indexing multi-dimensional uncertain data with arbitrary probability density functions
Get PDFResearch Session 26: Spatial and Temporal DatabasesIn an "uncertain database", an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value p q and a rectangular area r q, retrieves the objects that appear in r q with probabilities at least p q. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.postprintThe 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August-2 September 2005. In Proceedings of 31st VLDB, 2005, v. 3, p. 922-93
An analysis of the local optima storage capacity of Hopfield network based fitness function models
Get PDFA Hopfield Neural Network (HNN) with a new weight update rule can be treated as a second order Estimation of Distribution Algorithm (EDA) or Fitness Function Model (FFM) for solving optimisation problems. The HNN models promising solutions and has a capacity for storing a certain number of local optima as low energy attractors. Solutions are generated by sampling the patterns stored in the attractors. The number of attractors a network can store (its capacity) has an impact on solution diversity and, consequently solution quality. This paper introduces two new HNN learning rules and presents the Hopfield EDA (HEDA), which learns weight values from samples of the fitness function. It investigates the attractor storage capacity of the HEDA and shows it to be equal to that known in the literature for a standard HNN. The relationship between HEDA capacity and linkage order is also investigated
Kinetic proofreading of gene activation by chromatin remodeling
Full text linkGene activation in eukaryotes involves the concerted action of histone tail
modifiers, chromatin remodellers and transcription factors, whose precise
coordination is currently unknown. We demonstrate that the experimentally
observed interactions of the molecules are in accord with a kinetic
proofreading scheme. Our finding could provide a basis for the development of
quantitative models for gene regulation in eukaryotes based on the
combinatorical interactions of chromatin modifiers.Comment: 8 pages, 2 Figures; application adde
Sequence learning in Associative Neuronal-Astrocytic Network
Full text linkThe neuronal paradigm of studying the brain has left us with limitations in
both our understanding of how neurons process information to achieve biological
intelligence and how such knowledge may be translated into artificial
intelligence and its most brain-derived branch, neuromorphic computing.
Overturning our fundamental assumptions of how the brain works, the recent
exploration of astrocytes is revealing that these long-neglected brain cells
dynamically regulate learning by interacting with neuronal activity at the
synaptic level. Following recent experimental evidence, we designed an
associative, Hopfield-type, neuronal-astrocytic network and analyzed the
dynamics of the interaction between neurons and astrocytes. We show that
astrocytes were sufficient to trigger transitions between learned memories in
the neuronal component of the network. Further, we mathematically derived the
timing of the transitions that was governed by the dynamics of the
calcium-dependent slow-currents in the astrocytic processes. Overall, we
provide a brain-morphic mechanism for sequence learning that is inspired by,
and aligns with, recent experimental findings. To evaluate our model, we
emulated astrocytic atrophy and showed that memory recall becomes significantly
impaired after a critical point of affected astrocytes was reached. This
brain-inspired and brain-validated approach supports our ongoing efforts to
incorporate non-neuronal computing elements in neuromorphic information
processing.Comment: 8 pages, 5 figure
Is there a no-go theorem for superradiant quantum phase transitions in cavity and circuit QED ?
Get PDFIn cavity quantum electrodynamics (QED), the interaction between an atomic
transition and the cavity field is measured by the vacuum Rabi frequency
. The analogous term "circuit QED" has been introduced for Josephson
junctions, because superconducting circuits behave as artificial atoms coupled
to the bosonic field of a resonator. In the regime with comparable
to the two-level transition frequency, "superradiant" quantum phase transitions
for the cavity vacuum have been predicted, e.g. within the Dicke model. Here,
we prove that if the time-independent light-matter Hamiltonian is considered, a
superradiant quantum critical point is forbidden for electric dipole atomic
transitions due to the oscillator strength sum rule. In circuit QED, the
capacitive coupling is analogous to the electric dipole one: yet, such no-go
property can be circumvented by Cooper pair boxes capacitively coupled to a
resonator, due to their peculiar Hilbert space topology and a violation of the
corresponding sum rule
Macroscopic coherence of a single exciton state in a polydiacetylene organic quantum wire
Full text linkWe show that a single exciton state in an individual ordered conjugated
polymer chain exhibits macroscopic quantum spatial coherence reaching tens of
microns, limited by the chain length. The spatial coherence of the k=0 exciton
state is demonstrated by selecting two spatially separated emitting regions of
the chain and observing their interference.Comment: 12 pages with 2 figure
Weak pairwise correlations imply strongly correlated network states in a neural population
Get PDFBiological networks have so many possible states that exhaustive sampling is
impossible. Successful analysis thus depends on simplifying hypotheses, but
experiments on many systems hint that complicated, higher order interactions
among large groups of elements play an important role. In the vertebrate
retina, we show that weak correlations between pairs of neurons coexist with
strongly collective behavior in the responses of ten or more neurons.
Surprisingly, we find that this collective behavior is described quantitatively
by models that capture the observed pairwise correlations but assume no higher
order interactions. These maximum entropy models are equivalent to Ising
models, and predict that larger networks are completely dominated by
correlation effects. This suggests that the neural code has associative or
error-correcting properties, and we provide preliminary evidence for such
behavior. As a first test for the generality of these ideas, we show that
similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and
Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah
(http://cosyne.org
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