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                  BIO-CONTROL BY NEURAL NETWORKS
                       Summary of a Workshop
           supported by the National Science Foundation
                          George A. Bekey
                    Computer Scince Department
                 Uniersity of Southern California
                                and
                         Peter G. Katna
                         Program Director
              Bioengineering and Aiding the Disabled
                    National Science Foundation
                       Alexandria, Virginia
                          May 16-18, 1990
                    Participating NSF Programs:
                  Behavioral and Neural Sciences
              Bioengineering and Aiding the Disabled
                        Engineering Systems
                         Neuroengineering
                                 
                         TABLE OF CONTENTS
      I. Introduction
     II. Workshop Agenda
    III. Summary of Presentations
     IV. Summary of Recommendations
      V. List of Attendees
     VI. References
                          I. INTRODUCTION
    In the view of a number of investigators, there is an

increasing dichotomy between engineering research in artificial neural networks and physiological research on neural control mechanisms. In order to determine the state of the art in both the biological and engineering view of bio-control by neural networks, to isolate the major difficulties that hinder communication and block progress in the field and to identify those areas where focused research might be most beneficial, NSF sponsored a small invitational workshop.

    The specific goals of the workshop were as follows:
    1.   To determine the state of the art in control of
         physiological systems by neural networks. How mature is
         this field? Can current models yield any insight into the
         structure and function of living control systems, or
         should they be viewed as input-output models, with little
         or no isomorphism to the nervous system?
    2.   To determine whether artificial neural networks, intended
         to mimic natural control systems, can be used to control
         systems that include biological components. Are we ready
         to design control systems that draw upon our knowledge of
         how natural systems behave?
    3.   To identify major difficulties that block progress in
         this field. Are the difficulties conceptual or
         experimental? Do we lack mathematical, computational, or
         experimental tools? Are there fundamental gaps in
         knowledge which hinder further application of artificial
         neural nets to living systems, either for model-building
         or for artificial control systes?
    The workshop was held on  May 16-18, 1990 in Alexandria,

Virgini. The 32 participants included six NSFprogram directors, two representatives from NIH, and 24 neural network researchers from both the biological and engineering communities. The conference was chaired by Dr. George Bekey, and sponsored by thesday, May 17, 1990

8:30 am Introductions and Presentation of Workshop Goals

         George Bekey, University of Southern California,

Conference Chairman

         NSF Program Directors:
               
               Peter Katona
 Lazzaro, California Institute of Technology
         Chi-Sang Poon, Massachusetts Institute of Technology

12:00 pm Lunch

1:30 pm Process Control by Neural Networks

         Lyle Ungar, University of Pennsylvania
         T. J. McAvoy Grillner, Karolinska Institute

11:15 am Methodology and Trends in Modeling

         Herb Rauch, Lockheed

12:00 pm Lunch

1:00 pm Grup Discussions

2:30 pm Presentatons from Groups; Summary of Recommendations

4:00 pm Adjourpper and midde layers of the frog's

    spinal ord (while the leg is placed in differnt positions)
    generated a force field with an equilibrium point. The
    implications of this field on the organization of the spinal
    cord were disassachusetts)
    are using these ideas for the design of a new model of
    cerebellar function. [3]
    Issues involving the neural control of locomotion were also

discussed by Hillel Chiel and Sten Grillner.

    Hillel Chiel (Biolog  For example, some of the model neurons showed
    rhythmic bursts of activity ("pacemaker neurons") which were
    modulated by input from other model neurons.  In addition, the
    architecture of the neural net controlling locomotion was
  ynaptic
    connections was capable of exhibiting surprisingly complex
    behavior patterns. [7]
    Sten Grillner (Nobel Institute for Neurophyiology, Stockholm)
    Locomotion Control in the Swimming Eel
    Thelocomotor control s  that without simulation, it was not possible to evaluateif
    the experimentally established network could account for the
    known locomotor behavior in terms f segmental and
    intersegmental coordination. [8] [9] [10]
    Te autohis system, presented by Wade Rogers (DuPont
    Neural Computation roup), the vagal baroceptor reflex has
    also been modeled in VLSI by John Lazzaro (Department of
    Electrical Computer Engineering, University of Colorado-
    Boulder). . Feldman then discussed certain aspects of the control of
    respiration, primarily the generation of respiratory rhythms
    and the importance of various properties of the neurons
    involved in these systems. Distributed networks of coupled

model of the respiratory

    control system in which the input-output relationship of the
    brainstem respiratory controller was governed by an optimality
    criterion.  The latter measured both deviation from steady
    state values of arte  the cerebral cortex, which served as a "proxy" of the
    brainstem neural network. [15]  The results suggested that
    such compound optimization behavior was quite feasible within
    the CNS, both at the level of the brain stem and higher br neural nets in
    both feedforward and feedback control, inverse model adaptive
    control and other control algorithms were discussed. [17] [18]
    [19]
    Andrew Barto (Computer Science Department, Univ. of Mass.)  
    On Compute
    views on some of the important research issues in the field of
    modeling of neural networks.  These included questions on: (1)
    convergence properties of networks, (2) heuristic
    architectures for specific tasks, (3) adaptive archIV.  RECOMMENDATIONS
   Much of the work of the workshop was accomplished in three

subgroups which met following the major presentations. The groups first discussed the need for new biological data in engineering models of neural networks, as weligator

         support.
    b)   Post-doctoral/sabbatical support could be used to place
         biologists in engineering labs and vice versa; perhaps
         these could be supported as supplements to existing
         projects.

2.are needed

         for artificial neural networks:
         Model neurons should capture more of the richness of
         behavior patterns seen in biological experiments than the
         simple weighted-summer-with-sigmoid-nonlinearity thaccount for
        emergent behavior patterns as those found in living
         sysems (e.g.: sensory-motor interactions,
         plant-controller interctions, distributed control
         paradigms).
    c)   Improved mehods for idenof new engineering adaptive
         control systems based onbiological prototypes should be
         pursued:
         Enhancing living systems, e.g., prosthetics.
         Chemical process control, control of bioreactors.

3. Ways inrding electrodes.

         Muscle-type actuators.
         Better motion monitoring equipment; tendon and contact
         force gauge implants and joint-angle monitoring implantstems methodologies are

needed:

         System concepts; ssteresis.
         System level hypotheses to direct experiments.
                       V. LIST OF ATTENDEES
    Dr. Panos J. Antsaklis
    Department of Electrical
      and Computer Engineering
    Universityy
    Computer Science Department
    University of Southern California
    Los Angeles, CA  90089
    (213) 740-4501
    (213) 740-7285 (FAX)
    Dr. Emilio Bizzi
    Department of Brain & Cognitive Sciences
    E25-526
    Maic Institute
    San Luis Obispo, CA 93407
    (805) 756-2131
   
    Dr. Daniel Bullock
    Center for Adaptive Systems
    Boston University
    11 Cunnington Street
    Boston, MA  02215
    (617) 353-9486
    (617) 353-2more, MD  21205
    (301) 955-8334
    (301) 955-3623 (FAX)
    Dr. Sten Grillner
    Karolinska Institute
    The Nobel Institute for Neurophysiology
    Box 60400, S-104
    Stockholm, Sweden
    011-46-8-336059
    011-46-8- Department of Physiology
    Ward Building 5-319
    Northwestern University Medical School
    303 E Chicago Avenue
    Chicago, IL  60611
    (312) 503-8219
    (312) 503-5101 (FAX)
    Dr. Peter Katona
    Bioengineering Cambridge, MA  02439
    (617) 253-5769
    (617) 253-8000 (FAX)
    Dr. Thomas McAvoy
    Department of Chemical Engineering
    University of Maryland
    College Park, MD  20742
    (301) 454-2432
    (301) 454-0855 (FAX)

8-5405

    (617) 253-2514
    Dr. Herb Rauch
    Palo Alto Research Lab
    Lockheed 92-20/254E
    3251 Hanover Street
    Palo Alto, CA  94304
    (415) 424-2704
    (415) 424-2662 (FAX)
    Dr. David A. Robinson
    Rootn, DE  19880-0352
    (302) 695-7136
    (302) 695-9631 (FAX)
    Dr. Robert J. Sclabassi
    Department of Neurosurgery
    Universiy of Pittsburgh
    Pittsburgh, PA  15213
    (412) 692-5093
    (412) 692-5287 (FAX)
   tion
    Rom 1151, ECS/ENG
    1800 G Street, N.W.
    Washington, DC  20550
   (202) 357-9618
    
                          VI. REFERENCES
    1.   Massone, L., and Bizzi, E., "A neural network model for
   the cerebellum," Neural Networks for
         Control, Chapter 15, W.T. Miller, R.S. Sutton and P. J
         Werbos, (EdD., "A lesion study of a
         heterogenous artificial neural network for hexapod
         locomotion," Proc. IJCNN, I:n in bipeds, tetrapods
         and fish," The Handbook of Physiology, Sec. 1, Vol. 2:
         The Nervous System, Motor Control, pp. 1179- 1236, V.B.
         Brooks, (Ed.), Maryland: Waverly Press, 1981.
    9.   Matsushima, T. and GrillneIT Press, Chap:  Silicon Ba receptors
         modeling cardiovascular pressure transduction in ANALOG
         VLSI, Lazarro, John, Schwaber, James and Rogers, Wade.
    12.  Schwaber, J.S., Paton, J.F., Spyer, K.M., and Rogers,
         9, 1987.
    15.  Poon, C.S. and Younes, M., "Optimization on, C.S., "Adaptive neural network that subserves
         optimal homeostatic control of breathing," (submitted).
    17.  McAvoy, T.J., "Modeling chemical process systems via
              Networks for Control, T. Miller, R.S. Sutton, and P.J.
         Werbos (Eds), Cambridge, MIT Press, 1990.

21. Iberall, T., Liu, H., and Bekey, G.A., "Building a

         generic architecture for robot hand control," IEEE
     es during trajectory formation," 
         Psychological Review, 95, pp. 49-90, 1988.
    24.  Bullock, D. and Grossberg, S., "Spinal network
         computations enable independet control of muscle length
         and joint compliance," Adand Suzuki, R., "A hierarchical
         neural-network model for control and learning of
         voluntary movement," Biological Cybernetics, 57, pp. 169-
         185, 1987.
    28  Massone, L. and Bizzi, E., "On the role of input
    
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