Sequential information processing using time-delay connections in ontogenic CALM networks

Abstract

In this paper, a variant of the categorization-and-learning-module (CALM) network is presented that is not only capable of categorizing sequential information with feedback, but can adapt its resources to the current training set. In other words, the modules of the network may grow or shrink depending on the complexity of the presented sequence-set. In the original CALM algorithm, modules did not have access to activations from earlier stimulus presentations. To bypass this limitation, we introduced time-delay connections in CALM. These connections allow for a delayed propagation of activation, such that information at a given time will be available to a module at a later timestep. In addition, modules can autonomously add and remove resources depending on the structure and complexity of the task domain. The performance of this ontogenic CALM network with time-delay connections is demonstrated and analyzed using a sample set of overlapping sequences from an existing problem domain.