Neuromorphic technology refers to the science of understanding structures and processing mechanisms of the brain.

HNeT technology applies the power of holography to modeling of synthetic neuron cells (through application of non-linear phase coherence/decoherence principles).  Neuromorphic structures and the underlying holographic principles of operation provide a vast increase in capability for machine learning.

To provide a practical example, a neuromorphic assembly will track a human face in real time.  This assembly learns facial images by direct exposure, building within its memory all observed forms of an individual and can subsequently identify that individual within a crowd,.. it can even determine facial expressions such as smiling or frowning.  The functionality demonstrated by HNeT assemblies of this type approach and even exceed the limit of current technological capabilities.