Overview | HNet | Performance Aspects | Mathematics | Biology

The topology of a trained holographic neural assembly is shown below for a non-linear associative mapping, illustrated along three input dimensions (represented by x, y and time).  In practice, the holographic neural method learns such non-linear topologies along an unrestricted number of input dimensions (up to 16 Million in the case of HNet).

Each crest or valley within this topology defines an associative pattern (also called memory trace) that has been learned by the HNeT assembly.  The HNeT assembly interpolates smoothly among all associative patterns that have been learned.

There is no known technique capable of achieving this convergence characteristic for non-linear systems, other than the holographic neural method.

By comparison, the topology achieved through linear methods (such as linear regression) is defined by a flat plane within the N dimensions of the input space, restricting learning capacity for real world systems and data.  The issue here is that despite significant efforts in applying conventional neural network technologies, often little advantage is gained over simpler and faster linear regression techniques.