Overview | HNet | Performance Aspects | Mathematics | Biology

There are many other features concerning the HNeT technology.  A brief summary of some of these aspects are provided below:

Memory Decay

Adjusts the rate of memory decay within cortical cells.  This is performed through controlled attenuation in the magnitude of cortical memory elements.

Learning Rate

Adjusts the rate of learning.  By default learning rate is set to 100%, meaning that a new stimulus-response pattern is mapped or learned precisely following one training exposure.  Reduction of learning rate may slow the learning process however often improves generalization.

Intracellular Feedback

A much simplified description of the learning and recall process is provided on this website.  The HNeT system employs intracellular feedback of the generated response signal to facilitate convergence (recall error reduction) over multiple training epochs.

Spatial Temporal Learning

Cells modeled on the hippocampal structure are provided within HNeT.  These provide the ability to buffer temporally distributed signals, allowing downstream cortical structures to learn temporal patterns or episodic memory such as speech.

Neural Plasticity

This feature allows cells to optimize their synaptic inter-connections applying correlation in cortical memory magnitude. The cell actually builds an equation of state governing the learning environment.