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  • 1
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    Using data tagging to improve the performance of Kanerva's sparse distributed memory (1988)
    In:  CASI
    Details
    Publication Date: 2019-06-28
    Description: The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.
    Keywords: COMPUTER PROGRAMMING AND SOFTWARE
    Type: NASA-CR-184557 , RIACS-TR-88.1 , NAS 1.26:184557
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 2
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    Temporal learning in the cerebellum: The microcircuit model (1990)
    In:  CASI
    Details
    Publication Date: 2019-06-28
    Description: The cerebellum is that part of the brain which coordinates motor reflex behavior. To perform effectively, it must learn to generate specific motor commands at the proper times. We propose a fundamental circuit, called the MicroCircuit, which is the minimal ensemble of neurons both necessary and sufficient to learn timing. We describe how learning takes place in the MicroCircuit, which then explains the global behavior of the cerebellum as coordinated MicroCircuit behavior.
    Keywords: CYBERNETICS
    Type: NASA-CR-188873 , NAS 1.26:188873 , RIACS-TR-90-37
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 3
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    G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm (1991)
    In:  CASI
    Details
    Publication Date: 2019-06-28
    Description: G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
    Keywords: COMPUTER PROGRAMMING AND SOFTWARE
    Type: NASA-CR-188935 , NAS 1.26:188935 , RIACS-TR-91-10
    Format: application/pdf
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    NASA TECHNICAL REPORTS
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