3,082 research outputs found
Oscillator neural network model with distributed native frequencies
We study associative memory of an oscillator neural network with distributed
native frequencies. The model is based on the use of the Hebb learning rule
with random patterns (), and the distribution function of
native frequencies is assumed to be symmetric with respect to its average.
Although the system with an extensive number of stored patterns is not allowed
to get entirely synchronized, long time behaviors of the macroscopic order
parameters describing partial synchronization phenomena can be obtained by
discarding the contribution from the desynchronized part of the system. The
oscillator network is shown to work as associative memory accompanied by
synchronized oscillations. A phase diagram representing properties of memory
retrieval is presented in terms of the parameters characterizing the native
frequency distribution. Our analytical calculations based on the
self-consistent signal-to-noise analysis are shown to be in excellent agreement
with numerical simulations, confirming the validity of our theoretical
treatment.Comment: 9 pages, revtex, 6 postscript figures, to be published in J. Phys.
Phase resetting reveals network dynamics underlying a bacterial cell cycle
Genomic and proteomic methods yield networks of biological regulatory
interactions but do not provide direct insight into how those interactions are
organized into functional modules, or how information flows from one module to
another. In this work we introduce an approach that provides this complementary
information and apply it to the bacterium Caulobacter crescentus, a paradigm
for cell-cycle control. Operationally, we use an inducible promoter to express
the essential transcriptional regulatory gene ctrA in a periodic, pulsed
fashion. This chemical perturbation causes the population of cells to divide
synchronously, and we use the resulting advance or delay of the division times
of single cells to construct a phase resetting curve. We find that delay is
strongly favored over advance. This finding is surprising since it does not
follow from the temporal expression profile of CtrA and, in turn, simulations
of existing network models. We propose a phenomenological model that suggests
that the cell-cycle network comprises two distinct functional modules that
oscillate autonomously and couple in a highly asymmetric fashion. These
features collectively provide a new mechanism for tight temporal control of the
cell cycle in C. crescentus. We discuss how the procedure can serve as the
basis for a general approach for probing network dynamics, which we term
chemical perturbation spectroscopy (CPS)
Bibliometric Mapping of the Computational Intelligence Field
In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996Γ’β¬β2000 and 2001Γ’β¬β2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation
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