This thesis proposes a novel method for learning and pattern recognition. The algorithm presented
relies entirely on memory arranged in a custom hierarchical data structure which shifts
the workload from the processor to memory. The structure and functionality draw on biology
and neuroscience for inspiration while not losing sight of the inherent strengths and limitations
of modern computers. A hierarchy of learned nodes is built, stored, and used for recognition
without the need for complicated math or statistics. Recognition and prediction are inherent to
the hierarchy and require little additional computation, even for matching of partial patterns.
The experiments and results presented empirically demonstrate the robustness of memory-based
recognition of images
