Neuromorphic systems refers to the science of understanding and modelling the structure and processing mechanisms of the animal brain. Traditional neural networks have little or no resemblance to actual neurological structures; however more importantly, have been shown to be very limited in capability despite large efforts and many years of research. The HNeT technology applies the power of holography (through the application of phase coherence/decoherence principles) to modelling of synthetic neuron cells. Assemblies comprised of such cells have a one-to-one correspondence with the primary cell structures of the brain. Neuromorphic structures and the underlying holographic principles of operation provide truly real-time learning, and present a vast increase in (stimulus-response) memory storage capacity. To provide a practical example, a small neuromorphic assembly (4 cells) can locate and track human faces in real time. An assembly can learn facial images in real time, building within its memory all observed forms of an individual, and subsequently identify that individual within a crowd, even determine facial expression such as smiling or frowning, etc. This application is beyond the upper limit of technological capability when employing the most advanced conventional methods. Application of the "cerebellar" model reduces the above task to a rather straight-forward procedure. HNeT technology is not limited to face tracking / identification, but may be similarly applied to numerous areas within the medical sector, process control, automation/robotics, financial systems, etc. © 2008 AND Corporation