A higher order Bayesian neural network with spiking units.
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.
Lansner
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Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm, Sweden. ala@nada.kth.se
Holst
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D000200:Action Potentials / Q000502:physiology; D001499:Bayes Theorem; D016208:Databases, Factual; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D011336:Probability; D013269:Stochastic Processes