Complexity of reliability and constraint satisfaction in neural networks

P. Berman, I. Parberry, G. Schnitger

    Research output: Contribution to journalConference articlepeer-review

    Abstract

    The fault-tolerance of a new combinatorial neural network model, called the summation network, is investigated. The summation network uses processors which have n inputs, each labelled with a positive or negative integer weight of O(n log n) bits, and O(n log n) outputs. The outputs encode, in binary, the sum of the weights of the 'on' inputs to the processor. The processors are interconnected in a network with unlimited fan-in and fan-out. It is shown that fault-tolerance of the summation network is directly related to the product of error-probability and fan-in. Intuitively, the larger the fan-in, the more likely it is that an error in another part of the network will affect an otherwise correct processor.

    Original languageEnglish (US)
    Number of pages1
    JournalNeural Networks
    Volume1
    Issue number1 SUPPL
    DOIs
    StatePublished - Jan 1 1988
    EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
    Duration: Sep 6 1988Sep 10 1988

    All Science Journal Classification (ASJC) codes

    • Cognitive Neuroscience
    • Artificial Intelligence

    Fingerprint Dive into the research topics of 'Complexity of reliability and constraint satisfaction in neural networks'. Together they form a unique fingerprint.

    Cite this