💾 Archived View for spam.works › mirrors › textfiles › programming › biocontr.txt captured on 2023-11-14 at 11:48:05.
⬅️ Previous capture (2023-06-16)
-=-=-=-=-=-=-
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