


                    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
      



