37,582 research outputs found
Gauge Theory for Finite-Dimensional Dynamical Systems
Gauge theory is a well-established concept in quantum physics,
electrodynamics, and cosmology. This theory has recently proliferated into new
areas, such as mechanics and astrodynamics. In this paper, we discuss a few
applications of gauge theory in finite-dimensional dynamical systems with
implications to numerical integration of differential equations. We distinguish
between rescriptive and descriptive gauge symmetry. Rescriptive gauge symmetry
is, in essence, re-scaling of the independent variable, while descriptive gauge
symmetry is a Yang-Mills-like transformation of the velocity vector field,
adapted to finite-dimensional systems. We show that a simple gauge
transformation of multiple harmonic oscillators driven by chaotic processes can
render an apparently "disordered" flow into a regular dynamical process, and
that there exists a remarkable connection between gauge transformations and
reduction theory of ordinary differential equations. Throughout the discussion,
we demonstrate the main ideas by considering examples from diverse engineering
and scientific fields, including quantum mechanics, chemistry, rigid-body
dynamics and information theory
Comparing Information-Theoretic Measures of Complexity in Boltzmann Machines
In the past three decades, many theoretical measures of complexity have been
proposed to help understand complex systems. In this work, for the first time,
we place these measures on a level playing field, to explore the qualitative
similarities and differences between them, and their shortcomings.
Specifically, using the Boltzmann machine architecture (a fully connected
recurrent neural network) with uniformly distributed weights as our model of
study, we numerically measure how complexity changes as a function of network
dynamics and network parameters. We apply an extension of one such
information-theoretic measure of complexity to understand incremental Hebbian
learning in Hopfield networks, a fully recurrent architecture model of
autoassociative memory. In the course of Hebbian learning, the total
information flow reflects a natural upward trend in complexity as the network
attempts to learn more and more patterns.Comment: 16 pages, 7 figures; Appears in Entropy, Special Issue "Information
Geometry II
When Can Matrix Query Languages Discern Matrices?
We investigate when two graphs, represented by their adjacency matrices, can be distinguished by means of sentences formed in MATLANG, a matrix query language which supports a number of elementary linear algebra operators. When undirected graphs are concerned, and hence the adjacency matrices are real and symmetric, precise characterisations are in place when two graphs (i.e., their adjacency matrices) can be distinguished. Turning to directed graphs, one has to deal with asymmetric adjacency matrices. This complicates matters. Indeed, it requires to understand the more general problem of when two arbitrary matrices can be distinguished in MATLANG. We provide characterisations of the distinguishing power of MATLANG on real and complex matrices, and on adjacency matrices of directed graphs in particular. The proof techniques are a combination of insights from the symmetric matrix case and results from linear algebra and linear control theory
Oscillations, metastability and phase transitions in brain and models of cognition
Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition
Non-Gaussian fluctuations in stochastic models with absorbing barriers
The dynamics of a one-dimensional stochastic model is studied in presence of
an absorbing boundary. The distribution of fluctuations is analytically
characterized within the generalized van Kampen expansion, accounting for
higher order corrections beyond the conventional Gaussian approximation. The
theory is shown to successfully capture the non Gaussian traits of the sought
distribution returning an excellent agreement with the simulations, for {\it
all times} and arbitrarily {\it close} to the absorbing barrier. At large
times, a compact analytical solution for the distribution of fluctuations is
also obtained, bridging the gap with previous investigations, within the van
Kampen picture and without resorting to alternative strategies, as elsewhere
hypothesized.Comment: 2 figures, submitted to Phys. Rev. Let
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