EVENT-BASED INFERENCE AND LEARNING FOR STOCHASTIC SPIKING BAYESIAN NETWORKS

摘要

A method of performing event-based Bayesian inference and learning includes receiving input events at each node. The method also includes applying bias weights and/or connection weights to the input events to obtain intermediate values. The method further includes determining a node state based on the intermediate values. Further still, the method includes computing an output event rate representing a posterior probability based on the node state to generate output events according to a stochastic point process.