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Summary: Proc. Nati. Acad. Sci. USA
Vol. 88, pp. 4433-4437, May 1991
Neurobiology
A more biologically plausible learning rule for neural networks
(reinforcement learning/coordinate transformation/posterior parietal cortex/sensorimotor integration/Hebbian synapses)
PIETRO MAZZONIt*, RICHARD A. ANDERSENt§, AND MICHAEL I. JORDANt
tDepartment of Brain and Cognitive Sciences, and tHarvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology,
Cambridge, MA 02139
Communicated by Francis Crick, February 21, 1991 (receivedfor review October 19, 1990)
ABSTRACT Many recent studies haveused artificialneural
network algorithms to model how the brain might process
information. However, back-propagation learning, the method
that is generally used to train these networks, is distinctly
"unbiological." We describe here a more biologically plausible
learning rule, using reinforcement learning, which we have
applied to the problem of how area 7a in the posterior parietal
cortex ofmonkeys might represent visual space in head-centered
coordinates. The network behaves similarly to networks trained
by using back-propagation and to neurons recorded in area 7a.
These results show that a neural network does not require back
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