6,577 research outputs found
Control of complex networks requires both structure and dynamics
The study of network structure has uncovered signatures of the organization
of complex systems. However, there is also a need to understand how to control
them; for example, identifying strategies to revert a diseased cell to a
healthy state, or a mature cell to a pluripotent state. Two recent
methodologies suggest that the controllability of complex systems can be
predicted solely from the graph of interactions between variables, without
considering their dynamics: structural controllability and minimum dominating
sets. We demonstrate that such structure-only methods fail to characterize
controllability when dynamics are introduced. We study Boolean network
ensembles of network motifs as well as three models of biochemical regulation:
the segment polarity network in Drosophila melanogaster, the cell cycle of
budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in
Arabidopsis thaliana. We demonstrate that structure-only methods both
undershoot and overshoot the number and which sets of critical variables best
control the dynamics of these models, highlighting the importance of the actual
system dynamics in determining control. Our analysis further shows that the
logic of automata transition functions, namely how canalizing they are, plays
an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure
State Concentration Exponent as a Measure of Quickness in Kauffman-type Networks
We study the dynamics of randomly connected networks composed of binary
Boolean elements and those composed of binary majority vote elements. We
elucidate their differences in both sparsely and densely connected cases. The
quickness of large network dynamics is usually quantified by the length of
transient paths, an analytically intractable measure. For discrete-time
dynamics of networks of binary elements, we address this dilemma with an
alternative unified framework by using a concept termed state concentration,
defined as the exponent of the average number of t-step ancestors in state
transition graphs. The state transition graph is defined by nodes corresponding
to network states and directed links corresponding to transitions. Using this
exponent, we interrogate the dynamics of random Boolean and majority vote
networks. We find that extremely sparse Boolean networks and majority vote
networks with arbitrary density achieve quickness, owing in part to long-tailed
in-degree distributions. As a corollary, only relatively dense majority vote
networks can achieve both quickness and robustness.Comment: 6 figure
Generalised Compositional Theories and Diagrammatic Reasoning
This chapter provides an introduction to the use of diagrammatic language, or
perhaps more accurately, diagrammatic calculus, in quantum information and
quantum foundations. We illustrate the use of diagrammatic calculus in one
particular case, namely the study of complementarity and non-locality, two
fundamental concepts of quantum theory whose relationship we explore in later
part of this chapter.
The diagrammatic calculus that we are concerned with here is not merely an
illustrative tool, but it has both (i) a conceptual physical backbone, which
allows it to act as a foundation for diverse physical theories, and (ii) a
genuine mathematical underpinning, permitting one to relate it to standard
mathematical structures.Comment: To appear as a Springer book chapter chapter, edited by G.
Chirabella, R. Spekken
Computational core and fixed-point organisation in Boolean networks
In this paper, we analyse large random Boolean networks in terms of a
constraint satisfaction problem. We first develop an algorithmic scheme which
allows to prune simple logical cascades and under-determined variables,
returning thereby the computational core of the network. Second we apply the
cavity method to analyse number and organisation of fixed points. We find in
particular a phase transition between an easy and a complex regulatory phase,
the latter one being characterised by the existence of an exponential number of
macroscopically separated fixed-point clusters. The different techniques
developed are reinterpreted as algorithms for the analysis of single Boolean
networks, and they are applied to analysis and in silico experiments on the
gene-regulatory networks of baker's yeast (saccaromices cerevisiae) and the
segment-polarity genes of the fruit-fly drosophila melanogaster.Comment: 29 pages, 18 figures, version accepted for publication in JSTA
The GIST of Concepts
A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud
experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior
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