Abstract: An apparatus, article and method containing an artificial neural network that, after training, produces new trainable nodes such that input data representative of a first event and input data representative of a second event both activate a subset of the new trainable nodes. The artificial neural network can generate an output that is influenced by the input data of both events. In various embodiments, the new trainable nodes are sequentially produced and show decreasing trainability over time such that, at a particular point in time, newer produced nodes are more trainable than earlier produced nodes. The artificial neural network can be included in various embodiments of methods, apparatus and articles for use in predicting or profiling events.

Abstract: A method for determining a path through concept nodes. The method includes calculating a spatial cost function between adjacent concept nodes in a lower dimensional layout representation of a network of concepts in a n-dimensional space and determining a path that follows a minimum spatial cost function through the concept nodes. The spatial cost function may be used to predict a next node in the path. The method may also include receiving an origin concept node or a goal concept node.

Abstract: An apparatus, article and method containing an artificial neural network that, after training, produces new trainable nodes such that input data representative of a first event and input data representative of a second event both activate a subset of the new trainable nodes. The artificial neural network can generate an output that is influenced by the input data of both events. In various embodiments, the new trainable nodes are sequentially produced and show decreasing trainability over time such that, at a particular point in time, newer produced nodes are more trainable than earlier produced nodes. The artificial neural network can be included in various embodiments of methods, apparatus and articles for use in predicting or profiling events.