64,277 research outputs found
A framework for the local information dynamics of distributed computation in complex systems
The nature of distributed computation has often been described in terms of
the component operations of universal computation: information storage,
transfer and modification. We review the first complete framework that
quantifies each of these individual information dynamics on a local scale
within a system, and describes the manner in which they interact to create
non-trivial computation where "the whole is greater than the sum of the parts".
We describe the application of the framework to cellular automata, a simple yet
powerful model of distributed computation. This is an important application,
because the framework is the first to provide quantitative evidence for several
important conjectures about distributed computation in cellular automata: that
blinkers embody information storage, particles are information transfer agents,
and particle collisions are information modification events. The framework is
also shown to contrast the computations conducted by several well-known
cellular automata, highlighting the importance of information coherence in
complex computation. The results reviewed here provide important quantitative
insights into the fundamental nature of distributed computation and the
dynamics of complex systems, as well as impetus for the framework to be applied
to the analysis and design of other systems.Comment: 44 pages, 8 figure
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent
dynamics is challenging. Often, these agents operate in diverse, non-stationary
environments, where models rely on hand-crafted environment-specific features
to infer influential regions in the system's surroundings. To overcome the
limitations of these inflexible models, we present GP-LAPLACE, a technique for
locating sources and sinks from trajectories in time-varying fields. Using
Gaussian processes, we jointly infer a spatio-temporal vector field, as well as
canonical vector calculus operations on that field. Notably, we do this from
only agent trajectories without requiring knowledge of the environment, and
also obtain a metric for denoting the significance of inferred causal features
in the environment by exploiting our probabilistic method. To evaluate our
approach, we apply it to both synthetic and real-world GPS data, demonstrating
the applicability of our technique in the presence of multiple agents, as well
as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference
on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures,
conference submission, University of Oxford. arXiv admin note: text overlap
with arXiv:1709.0235
Probability as a physical motive
Recent theoretical progress in nonequilibrium thermodynamics, linking the
physical principle of Maximum Entropy Production ("MEP") to the
information-theoretical "MaxEnt" principle of scientific inference, together
with conjectures from theoretical physics that there may be no fundamental
causal laws but only probabilities for physical processes, and from
evolutionary theory that biological systems expand "the adjacent possible" as
rapidly as possible, all lend credence to the proposition that probability
should be recognized as a fundamental physical motive. It is further proposed
that spatial order and temporal order are two aspects of the same thing, and
that this is the essence of the second law of thermodynamics.Comment: Replaced at the request of the publisher. Minor corrections to
references and to Equation 1 added
Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks
Properties of networks are often characterized in terms of features such as
node degree distributions, average path lengths, diameters, or clustering
coefficients. Here, we study shortest path length distributions. On the one
hand, average as well as maximum distances can be determined therefrom; on the
other hand, they are closely related to the dynamics of network spreading
processes. Because of the combinatorial nature of networks, we apply maximum
entropy arguments to derive a general, physically plausible model. In
particular, we establish the generalized Gamma distribution as a continuous
characterization of shortest path length histograms of networks or arbitrary
topology. Experimental evaluations corroborate our theoretical results
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in Drosophila
We apply techniques from the field of computational mechanics to evaluate the
statistical complexity of neural recording data from fruit flies. First, we
connect statistical complexity to the flies' level of conscious arousal, which
is manipulated by general anesthesia (isoflurane). We show that the complexity
of even single channel time series data decreases under anesthesia. The
observed difference in complexity between the two states of conscious arousal
increases as higher orders of temporal correlations are taken into account. We
then go on to show that, in addition to reducing complexity, anesthesia also
modulates the informational structure between the forward- and reverse-time
neural signals. Specifically, using three distinct notions of temporal
asymmetry we show that anesthesia reduces temporal asymmetry on
information-theoretic and information-geometric grounds. In contrast to prior
work, our results show that: (1) Complexity differences can emerge at very
short timescales and across broad regions of the fly brain, thus heralding the
macroscopic state of anesthesia in a previously unforeseen manner, and (2) that
general anesthesia also modulates the temporal asymmetry of neural signals.
Together, our results demonstrate that anesthetized brains become both less
structured and more reversible.Comment: 14 pages, 6 figures. Comments welcome; Added time-reversal analysis,
updated discussion, new figures (Fig. 5 & Fig. 6) and Tables (Tab. 1
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
Metastability, Criticality and Phase Transitions in brain and its Models
This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures
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