Learning of invariant object recognition in hierarchical neural networks using temporal continuity

Advisor: Rolf P. Würtz, Institute for Neural Computation, Ruhr-University Bochum, Germany. Date and location of PhD thesis defense: 3 November 2014, Ruhr-University Bochum, GermanyThere has been a lot of progress in the field of invariant object recognition/categorization in the last decade with several methods trying to mimic functioning of the human visual system (e.g. Neocognitron, HMAX, VisNet). Examining those brain regions is a very difficult task with myriads of details to be considered. To simplify modeling approaches, Jeff Hawkins [1] proposed a framework of three basic principles that might underlie computations in regions of the neocortex. These also form the basis for a capable object recognition system named "Hierarchical Temporal Memory" (HTM). 1. Learning of temporal sequences for creating invariance to transformations contained in the training data. 2. Learning in a hierarchical structure, in which lower level knowledge can be reused in higher level context and thereby makes memory usage efficient. 3. Prediction of future signals for disambiguation of noisy input by feedback