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