The following sections summarize a number of performance aspects that are characteristic of holographic neural processing. This material is intended for a technical or engineering audience.
Learning Capacity |
Summarizes performance aspects pertaining to speed of stimulus-response learning and related memory storage capacity, in comparison to conventional neural networks. |
Convergence |
Illustrates the convergence characteristics of holographic learning when applying multiple training exposures or “epochs”. |
Generalization |
Describes generalization aspects when the learning environment is highly complex or "non-linear". |
Neural Plasticity |
Describes the process of neural plasticity (synaptic pruning and regrowth) and the performance gained through optimization of complex combinatorics. |
Computational Complexity |
Defines the number of numerical operations or hardware registers (Complex Multiply and Accumulate) that are required to execute a neuro-holographic assembly. |
