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Tech Reports from CBCL at MIT

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  • Tech Reports from CBCL at MIT

    The Center for Biological and Computational Learning (CBCL) is a
    newly formed organization at the Dept. of Brain and Cognitive Sciences
    at M.I.T. The Center's aim is to pursue projects which look at
    learning from a systems perspective, linking the neurophysiology
    of learning with its computational, mathematical, and conceptual
    components in areas of motor control, vision, speech, and language.

    Some of the work of the members of the Center is now available
    in the form of technical reports. These reports are published
    in conjuction with the AI Memo series. You can get a copy of these
    reports via anonymous ftp (see the end of this message for details).

    Here is a list of titles currently available via ftp:

    :CBCL Paper #79/AI Memo #1390
    :author Jose L. Marroquin and Federico Girosi
    :title Some Extensions of the K-Means Algorithm for Image Segmentation
    and Pattern Classification
    :date January 1993
    ages 21
    :keywords K-means, clustering, vector quantization, segmentation,
    We present some extensions to the k-means algorithm for vector
    quantization that permit its efficient use in image segmentation and
    pattern classification tasks. We show that by introducing a certain
    set of state variables it is possible to find the representative
    centers of the lower dimensional manifolds that define the boundaries
    between classes; this permits one, for example, to find class
    boundaries directly from sparse data or to efficiently place centers
    for pattern classification. The same state variables can be used to
    determine adaptively the optimal number of centers for clouds of data
    with space-varying density. Some examples of the application of these
    extensions are also given.

    :CBCL Paper #82/AI Memo #1437
    :author Reza Shadmehr and Ferdinando A. Mussa-Ivaldi
    :title Geometric Structure of the Adaptive Controller of the Human Arm
    :date July 1993
    ages 34
    :keywords Motor learning, reaching movements, internal models, force fields,
    virtual environments, generalization, motor control.
    The objects with which the hand interacts with may significantly change the
    dynamics of the arm. How does the brain adapt control of arm movements
    to this new dynamics? We show that adaptation is via composition of a
    model of the task's dynamics. By exploring generalization capabilities
    of this adaptation we infer some of the properties of the computational
    elements with which the brain formed this model: the elements have broad
    receptive fields and encode the learned dynamics as a map structured in an
    intrinsic coordinate system closely related to the geometry of the
    skeletomusculature. The low--level nature of these elements suggests that
    they may represent a set of primitives with which movement are represented
    in the CNS.


    How to get a copy of above reports:

    The files are in compressed postscript format and are named by their
    AI memo number, e.g., the Shadmehr and Mussa-Ivaldi paper is named They are put in a directory named as the year
    in which the paper was written.

    Here is the procedure for ftp-ing:

    unix> ftp (log-in as anonymous)
    ftp> cd ai-pubs/publications/1993
    ftp> binary
    ftp> get
    ftp> quit
    unix> zcat | lpr


    I will periodically update the above list as new titles become

    Best wishes,

    Reza Shadmehr
    Center for Biological and Computational Learning
    M. I. T.
    Cambridge, MA 02139