You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Overlapping Neural Networks for Multiple Motor Engrams
Alexander V. Lukashin, George L. Wilcox and Apostolos P. Georgopoulos
Proceedings of the National Academy of Sciences of the United States of America
Vol. 91, No. 18 (Aug. 30, 1994), pp. 8651-8654
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/2365451
Page Count: 4
You can always find the topics here!Topics: Trajectories, Motors, Motor ability, Brain, Biological neural networks, Memory trace, Mathematical vectors, Neurons, Simulation training, Simulated annealing
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
The hypothesis was tested that learned movement trajectories of different shapes can be stored in, and generated by, largely overlapping neural networks. Indeed, it was possible to train a massively interconnected neural network to generate different shapes of internally stored, dynamically evolving movement trajectories using a general-purpose core part, common to all networks, and a special-purpose part, specific for a particular trajectory. The weights of connections between the core units do not carry any information about trajectories. The core network alone could generate externally instructed trajectories but not internally stored ones, for which both the core and the trajectory-specific part were needed. All information about the movements is stored in the weights of connections between the core part and the specialized units and between the specialized units themselves. Due to these connections the core part reveals specific dynamical behavior for a particular trajectory and, as the result, discriminates different tasks. The percentage of trajectory-specific units needed to generate a certain trajectory was small (2-5%), and the total output of the network is almost entirely provided by the core part, whereas the role of the small specialized parts is to drive the dynamical behavior. These results suggest an efficient and effective mechanism for storing learned motor patterns in, and reproducing them by, overlapping neural networks and are in accord with neurophysiological findings of trajectory-specific cells and with neurological observations of loss of specific motor skills in the presence of otherwise intact motor control.
Proceedings of the National Academy of Sciences of the United States of America © 1994 National Academy of Sciences