This concludes the background information.

The present neural-network model incorporates biological spike coding along with some basic principles of the learning by synapses in the cortex of the human brain. According to the learning rule of the model, synaptic weights are adapted when pre- and postsynaptic spikes occur within short time windows. In simplified terms, for a given synapse and time window, the synaptic strength is increased in the long term if the presynaptic spike precedes the postsynaptic spike or is decreased in the long term if the presynaptic spike follows the postsynaptic spike. This learning rule has been shown to minimize prediction errors, indicating that the neural network learns an optimal dynamic model of an external process.

This work was done by Tuan Duong, Vu Duong, and Roland Suri of Caltech for NASA’s Jet Propulsion Laboratory.

In accordance with Public Law 96-517, the contractor has elected to retain title to this invention. Inquiries concerning rights for its commercial use should be addressed to:

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Refer to NPO-41691, volume and number of this NASA Tech Briefs issue, and the page number.

NASA Tech Briefs Magazine

This article first appeared in the July, 2009 issue of NASA Tech Briefs Magazine.

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