134,066 research outputs found
Bits from Biology for Computational Intelligence
Computational intelligence is broadly defined as biologically-inspired
computing. Usually, inspiration is drawn from neural systems. This article
shows how to analyze neural systems using information theory to obtain
constraints that help identify the algorithms run by such systems and the
information they represent. Algorithms and representations identified
information-theoretically may then guide the design of biologically inspired
computing systems (BICS). The material covered includes the necessary
introduction to information theory and the estimation of information theoretic
quantities from neural data. We then show how to analyze the information
encoded in a system about its environment, and also discuss recent
methodological developments on the question of how much information each agent
carries about the environment either uniquely, or redundantly or
synergistically together with others. Last, we introduce the framework of local
information dynamics, where information processing is decomposed into component
processes of information storage, transfer, and modification -- locally in
space and time. We close by discussing example applications of these measures
to neural data and other complex systems
Reconciling Synthesis and Decomposition: A Composite Approach to Capability Identification
Stakeholders' expectations and technology constantly evolve during the
lengthy development cycles of a large-scale computer based system.
Consequently, the traditional approach of baselining requirements results in an
unsatisfactory system because it is ill-equipped to accommodate such change. In
contrast, systems constructed on the basis of Capabilities are more
change-tolerant; Capabilities are functional abstractions that are neither as
amorphous as user needs nor as rigid as system requirements. Alternatively,
Capabilities are aggregates that capture desired functionality from the users'
needs, and are designed to exhibit desirable software engineering
characteristics of high cohesion, low coupling and optimum abstraction levels.
To formulate these functional abstractions we develop and investigate two
algorithms for Capability identification: Synthesis and Decomposition. The
synthesis algorithm aggregates detailed rudimentary elements of the system to
form Capabilities. In contrast, the decomposition algorithm determines
Capabilities by recursively partitioning the overall mission of the system into
more detailed entities. Empirical analysis on a small computer based library
system reveals that neither approach is sufficient by itself. However, a
composite algorithm based on a complementary approach reconciling the two polar
perspectives results in a more feasible set of Capabilities. In particular, the
composite algorithm formulates Capabilities using the cohesion and coupling
measures as defined by the decomposition algorithm and the abstraction level as
determined by the synthesis algorithm.Comment: This paper appears in the 14th Annual IEEE International Conference
and Workshop on the Engineering of Computer Based Systems (ECBS); 10 pages, 9
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Measuring multivariate redundant information with pointwise common change in surprisal
The problem of how to properly quantify redundant information is an open question that has been the subject of much recent research. Redundant information refers to information about a target variable S that is common to two or more predictor variables Xi . It can be thought of as quantifying overlapping information content or similarities in the representation of S between the Xi . We present a new measure of redundancy which measures the common change in surprisal shared between variables at the local or pointwise level. We provide a game-theoretic operational definition of unique information, and use this to derive constraints which are used to obtain a maximum entropy distribution. Redundancy is then calculated from this maximum entropy distribution by counting only those local co-information terms which admit an unambiguous interpretation as redundant information. We show how this redundancy measure can be used within the framework of the Partial Information Decomposition (PID) to give an intuitive decomposition of the multivariate mutual information into redundant, unique and synergistic contributions. We compare our new measure to existing approaches over a range of example systems, including continuous Gaussian variables. Matlab code for the measure is provided, including all considered examples
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