View Full Version : Tech Reports from CBCL at MIT

Reza Shadmehr
07-26-1993, 10:17 PM
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
:pages 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
:pages 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
AIM-1437.ps.Z. 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 ftp.ai.mit.edu (log-in as anonymous)
ftp> cd ai-pubs/publications/1993
ftp> binary
ftp> get AIM-number.ps.Z
ftp> quit
unix> zcat AIM-number.ps.Z | 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