Controlling deterministic output variability in a feature extracting chaotic BAM

In this work, a chaotic feature extracting BAM that is capable of generating various behaviors is introduced. These behaviors arise from different attractors, ranging from a stored fixed point to a wandering chaotic region, including variations of all stored fixed points. Variations of stored patterns are generated by the network via the setting of the variability exhibited by every extracted feature. A control method is applied in order to move the network's trajectory into the desired regions and generate chaotic itinerancy, which is reported as a salient property of the brain system. This control is achieved by adjusting the free parameters of the feature extracting units' activation functions. Moreover, it is shown that the higher the number of units applied as feature extractors, the more local features are obtained, control of which leads to greater output uncertainty. However, the structure of this model is very simple and its complex behavior is a result of the interaction among feature units. These observations imply that the proposed model can be feasibly applied in information processing, such as searching in memory, pattern recognition in the presence of noise and variability, modeling episodic memory and decision making in a changing environment.