Although the mathematical basis behind HNeT is somewhat abstract, one does not require an in-depth understanding of the theory in order to design and build applications using the HNet Application Development System.  It is important that one understands how information is presented to the system, and how the various classes of holographic based neuron cells interface with each other.

A stimulus-response pattern, association or "memory" may be represented by a set of values, reflecting conditions or states measured within an external environment, such as pressure, temperature, brightness, etc.  During learning, neural cells associate or "map" one set of analog values (i.e. the stimulus fields) to an associated set of values (i.e. the responses).  When stimuli are distributed over a time span, one has spatial-temporal or episodic learning.

The mathematical basis for HNeT permits vast numbers of stimulus-response or “associative” patterns to be learned and superimposed onto a vector comprised of complex scalars, called the cell's cortical memory.  In fact, the number of values used to store cortical memory is often no larger than the number of elements within a single stimuli.  The mechanism for holographic storage displays a capacity to achieve extremely large information densities, due to the fact that large numbers of associative memories are superimposed or enfolded onto the same set of storage elements (in other words - computer RAM).

Again, the material provided in subsequent pages is intended for a technical audience.