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Representation and Decision Making in the Immune System.

McEwan, Chris (2010) Representation and Decision Making in the Immune System. PhD thesis, Edinburgh Napier University.

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    Abstract/Description

    The immune system has long been attributed cognitive capacities such as
    "recognition" of pathogenic agents; "memory" of
    previous infections; "regulation" of a cavalry of detector and
    effector cells; and "adaptation" to a changing environment and
    evolving threats. Ostensibly, in preventing disease the immune system must be capable of discriminating states of pathology in the organism; identifying causal agents or ``pathogens''; and correctly deploying lethal effector mechanisms. What is
    more, these behaviours must be learnt insomuch as the paternal genes cannot
    encode the pathogenic environment of the child.
    Insights into the mechanisms underlying these phenomena are of
    interest, not only to immunologists, but to computer scientists pushing the
    envelope of machine autonomy.

    This thesis approaches these phenomena from the perspective that immunological
    processes are inherently inferential processes. By considering the immune system
    as a statistical decision maker, we attempt to build a bridge between
    the traditionally distinct fields of biological modelling and statistical
    modelling. Through a mixture of novel theoretical and empirical analysis we
    assert the efficacy of competitive exclusion as a general principle
    that benefits both. For the immunologist, the statistical modelling perspective
    allows us to better determine that which is phenomenologically sufficient from
    the mass of observational data, providing quantitative insight that may offer
    relief from existing dichotomies. For the computer scientist, the biological
    modelling perspective results in a theoretically transparent and empirically
    effective numerical method that is able to finesse the trade-off between myopic
    greediness and intractability in domains such as sparse approximation, continuous learning and boosting weak heuristics. Together, we offer this as a modern reformulation of the interface between computer science and immunology, established in the seminal work of Perelson and collaborators, over 20 years ago.

    Item Type: Thesis (PhD)
    Uncontrolled Keywords: Immune system; cognitive capacities; mechanisms; inferential processes; biological modelling; statistical modelling; novel theoretical analysis; empirical analysis;
    University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
    Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 003 Systems > 003.3 Computer modelling & simulation
    500 Science > 510 Mathematics > 518 Numerical analysis
    Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Item ID: 4157
    Depositing User: Mr Chris McEwan
    Date Deposited: 02 Feb 2011 11:37
    Last Modified: 25 Aug 2011 10:23
    URI: http://researchrepository.napier.ac.uk/id/eprint/4157

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