407 lines
12 KiB
Plaintext
407 lines
12 KiB
Plaintext
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BIO-CONTROL BY NEURAL NETWORKS
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Summary of a Workshop
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supported by the National Science Foundation
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George A. Bekey
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Computer Scince Department
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Uniersity of Southern California
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and
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Peter G. Katna
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Program Director
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Bioengineering and Aiding the Disabled
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National Science Foundation
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Alexandria, Virginia
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May 16-18, 1990
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Participating NSF Programs:
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Behavioral and Neural Sciences
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Bioengineering and Aiding the Disabled
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Engineering Systems
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Neuroengineering
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TABLE OF CONTENTS
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I. Introduction
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II. Workshop Agenda
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III. Summary of Presentations
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IV. Summary of Recommendations
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V. List of Attendees
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VI. References
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I. INTRODUCTION
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In the view of a number of investigators, there is an
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increasing dichotomy between engineering research in artificial
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neural networks and physiological research on neural control
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mechanisms. In order to determine the state of the art in both the
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biological and engineering view of bio-control by neural networks,
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to isolate the major difficulties that hinder communication and
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block progress in the field and to identify those areas where
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focused research might be most beneficial, NSF sponsored a small
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invitational workshop.
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The specific goals of the workshop were as follows:
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1. To determine the state of the art in control of
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physiological systems by neural networks. How mature is
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this field? Can current models yield any insight into the
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structure and function of living control systems, or
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should they be viewed as input-output models, with little
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or no isomorphism to the nervous system?
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2. To determine whether artificial neural networks, intended
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to mimic natural control systems, can be used to control
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systems that include biological components. Are we ready
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to design control systems that draw upon our knowledge of
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how natural systems behave?
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3. To identify major difficulties that block progress in
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this field. Are the difficulties conceptual or
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experimental? Do we lack mathematical, computational, or
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experimental tools? Are there fundamental gaps in
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knowledge which hinder further application of artificial
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neural nets to living systems, either for model-building
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or for artificial control systes?
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The workshop was held on May 16-18, 1990 in Alexandria,
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Virgini. The 32 participants included six NSFprogram directors,
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two representatives from NIH, and 24 neural network researchers
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from both the biological and engineering communities. The
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conference was chaired by Dr. George Bekey, and sponsored by
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thesday, May 17, 1990
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8:30 am Introductions and Presentation of Workshop Goals
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George Bekey, University of Southern California,
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Conference Chairman
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NSF Program Directors:
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Peter Katona
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Lazzaro, California Institute of Technology
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Chi-Sang Poon, Massachusetts Institute of Technology
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12:00 pm Lunch
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1:30 pm Process Control by Neural Networks
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Lyle Ungar, University of Pennsylvania
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T. J. McAvoy Grillner, Karolinska Institute
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11:15 am Methodology and Trends in Modeling
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Herb Rauch, Lockheed
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12:00 pm Lunch
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1:00 pm Grup Discussions
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2:30 pm Presentatons from Groups; Summary of Recommendations
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4:00 pm Adjourpper and midde layers of the frog's
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spinal ord (while the leg is placed in differnt positions)
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generated a force field with an equilibrium point. The
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implications of this field on the organization of the spinal
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cord were disassachusetts)
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are using these ideas for the design of a new model of
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cerebellar function. [3]
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Issues involving the neural control of locomotion were also
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discussed by Hillel Chiel and Sten Grillner.
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Hillel Chiel (Biolog For example, some of the model neurons showed
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rhythmic bursts of activity ("pacemaker neurons") which were
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modulated by input from other model neurons. In addition, the
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architecture of the neural net controlling locomotion was
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ynaptic
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connections was capable of exhibiting surprisingly complex
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behavior patterns. [7]
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Sten Grillner (Nobel Institute for Neurophyiology, Stockholm)
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Locomotion Control in the Swimming Eel
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Thelocomotor control s that without simulation, it was not possible to evaluateif
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the experimentally established network could account for the
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known locomotor behavior in terms f segmental and
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intersegmental coordination. [8] [9] [10]
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Te autohis system, presented by Wade Rogers (DuPont
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Neural Computation roup), the vagal baroceptor reflex has
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also been modeled in VLSI by John Lazzaro (Department of
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Electrical Computer Engineering, University of Colorado-
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Boulder). . Feldman then discussed certain aspects of the control of
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respiration, primarily the generation of respiratory rhythms
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and the importance of various properties of the neurons
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involved in these systems. Distributed networks of coupled
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model of the respiratory
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control system in which the input-output relationship of the
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brainstem respiratory controller was governed by an optimality
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criterion. The latter measured both deviation from steady
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state values of arte the cerebral cortex, which served as a "proxy" of the
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brainstem neural network. [15] The results suggested that
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such compound optimization behavior was quite feasible within
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the CNS, both at the level of the brain stem and higher br neural nets in
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both feedforward and feedback control, inverse model adaptive
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control and other control algorithms were discussed. [17] [18]
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[19]
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Andrew Barto (Computer Science Department, Univ. of Mass.)
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On Compute
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views on some of the important research issues in the field of
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modeling of neural networks. These included questions on: (1)
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convergence properties of networks, (2) heuristic
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architectures for specific tasks, (3) adaptive archIV. RECOMMENDATIONS
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Much of the work of the workshop was accomplished in three
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subgroups which met following the major presentations. The groups
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first discussed the need for new biological data in engineering
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models of neural networks, as weligator
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support.
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b) Post-doctoral/sabbatical support could be used to place
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biologists in engineering labs and vice versa; perhaps
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these could be supported as supplements to existing
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projects.
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2.are needed
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for artificial neural networks:
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Model neurons should capture more of the richness of
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behavior patterns seen in biological experiments than the
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simple weighted-summer-with-sigmoid-nonlinearity thaccount for
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emergent behavior patterns as those found in living
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sysems (e.g.: sensory-motor interactions,
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plant-controller interctions, distributed control
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paradigms).
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c) Improved mehods for idenof new engineering adaptive
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control systems based onbiological prototypes should be
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pursued:
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Enhancing living systems, e.g., prosthetics.
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Chemical process control, control of bioreactors.
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3. Ways inrding electrodes.
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Muscle-type actuators.
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Better motion monitoring equipment; tendon and contact
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force gauge implants and joint-angle monitoring implantstems methodologies are
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needed:
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System concepts; ssteresis.
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System level hypotheses to direct experiments.
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V. LIST OF ATTENDEES
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Dr. Panos J. Antsaklis
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Department of Electrical
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and Computer Engineering
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Universityy
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Computer Science Department
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University of Southern California
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Los Angeles, CA 90089
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(213) 740-4501
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(213) 740-7285 (FAX)
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Dr. Emilio Bizzi
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Department of Brain & Cognitive Sciences
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E25-526
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Maic Institute
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San Luis Obispo, CA 93407
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(805) 756-2131
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Dr. Daniel Bullock
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Center for Adaptive Systems
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Boston University
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11 Cunnington Street
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Boston, MA 02215
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(617) 353-9486
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(617) 353-2more, MD 21205
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(301) 955-8334
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(301) 955-3623 (FAX)
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Dr. Sten Grillner
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Karolinska Institute
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The Nobel Institute for Neurophysiology
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Box 60400, S-104
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Stockholm, Sweden
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011-46-8-336059
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011-46-8- Department of Physiology
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Ward Building 5-319
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Northwestern University Medical School
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303 E Chicago Avenue
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Chicago, IL 60611
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(312) 503-8219
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(312) 503-5101 (FAX)
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Dr. Peter Katona
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Bioengineering Cambridge, MA 02439
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(617) 253-5769
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(617) 253-8000 (FAX)
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Dr. Thomas McAvoy
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Department of Chemical Engineering
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University of Maryland
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College Park, MD 20742
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(301) 454-2432
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(301) 454-0855 (FAX)
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8-5405
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(617) 253-2514
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Dr. Herb Rauch
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Palo Alto Research Lab
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Lockheed 92-20/254E
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3251 Hanover Street
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Palo Alto, CA 94304
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(415) 424-2704
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(415) 424-2662 (FAX)
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Dr. David A. Robinson
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Rootn, DE 19880-0352
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(302) 695-7136
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(302) 695-9631 (FAX)
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Dr. Robert J. Sclabassi
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Department of Neurosurgery
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Universiy of Pittsburgh
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Pittsburgh, PA 15213
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(412) 692-5093
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(412) 692-5287 (FAX)
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tion
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Rom 1151, ECS/ENG
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1800 G Street, N.W.
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Washington, DC 20550
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(202) 357-9618
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VI. REFERENCES
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1. Massone, L., and Bizzi, E., "A neural network model for
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the cerebellum," Neural Networks for
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Control, Chapter 15, W.T. Miller, R.S. Sutton and P. J
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Werbos, (EdD., "A lesion study of a
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heterogenous artificial neural network for hexapod
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locomotion," Proc. IJCNN, I:n in bipeds, tetrapods
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and fish," The Handbook of Physiology, Sec. 1, Vol. 2:
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The Nervous System, Motor Control, pp. 1179- 1236, V.B.
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Brooks, (Ed.), Maryland: Waverly Press, 1981.
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9. Matsushima, T. and GrillneIT Press, Chap: Silicon Ba receptors
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modeling cardiovascular pressure transduction in ANALOG
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VLSI, Lazarro, John, Schwaber, James and Rogers, Wade.
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12. Schwaber, J.S., Paton, J.F., Spyer, K.M., and Rogers,
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9, 1987.
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15. Poon, C.S. and Younes, M., "Optimization on, C.S., "Adaptive neural network that subserves
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optimal homeostatic control of breathing," (submitted).
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17. McAvoy, T.J., "Modeling chemical process systems via
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Networks for Control, T. Miller, R.S. Sutton, and P.J.
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Werbos (Eds), Cambridge, MIT Press, 1990.
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21. Iberall, T., Liu, H., and Bekey, G.A., "Building a
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generic architecture for robot hand control," IEEE
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es during trajectory formation,"
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Psychological Review, 95, pp. 49-90, 1988.
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24. Bullock, D. and Grossberg, S., "Spinal network
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computations enable independet control of muscle length
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and joint compliance," Adand Suzuki, R., "A hierarchical
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neural-network model for control and learning of
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voluntary movement," Biological Cybernetics, 57, pp. 169-
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185, 1987.
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28 Massone, L. and Bizzi, E., "On the role of input
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