1. A system configured to analyzing at least one data pattern comprising: an input configured to receive at least one data pattern; at least one hierarchical neural network, having a plurality of hierarchical layers, each respective hierarchical layer being configured to receive a respective input and to produce a non-arbitrary organization of actions in dependence on the respective input and a respective layer training, the at least one hierarchical neural network comprising: a first layer configured to produce a non-arbitrary organization of actions which identifies at least one data object from a plurality of data objects, based at least the first layer training to identify a plurality of different data objects, and the at least one data pattern, and to produce a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, a second layer, configured to receive the respective non-arbitrary organization of actions from the first layer identifying the object as the respective input, based on the non-arbitrary organization of actions from the first layer, to ascertain a type of the identified data object from a plurality of different types of each of the plurality of different data objects; wherein the first layer further produces a noise vector output, distinct from the non-arbitrary organization of actions of the first layer, representing a deviance of at least a portion of the at least one data pattern from a prototype of the data object identified, and a processor configured to at least one of: determine a confidence of data object identification, and determine that the data pattern comprises a data object not properly identified by the first layer.