A higher order Bayesian neural network with spiking units.

A Lansner; A Holst

doi:10.1142/s0129065796000816

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A higher order Bayesian neural network with spiking units.

A Lansner·A Holst

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Abstract

We treat a Bayesian confidence propagation neural network, primarily in a classifier context. The one-layer version of the network implements a naive Bayesian classifier, which requires the input attributes to be independent. This limitation is overcome by a higher order network. The higher order Bayesian neural network is evaluated on a real world task of diagnosing a telephone exchange computer. By introducing stochastic spiking units, and soft interval coding, it is also possible to handle uncertain as well as continuous valued inputs.

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Action Potentials Bayes Theorem Databases, Factual Neural Networks, Computer Neurons Probability Stochastic Processes Journal Article Research Support, Non-U.S. Gov't Review

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A Lansner, A Holst. 1996. A higher order Bayesian neural network with spiking units.. https://doi.org/10.1142/s0129065796000816

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