We have constructed an application development for this technology, allowing one to build neuromorphic assemblies and integrate their capabilities into real world applications.   This system is called HNet.

The HNet API provides a high level of flexibility in the construction and execution of neuromorphic assemblies, as well as application level integration, using Windows based development tools such as Visual C and Java.  This application development system consists of the following two primary components.

The HNeT Supervised Learning (SL) Platform provides an advanced GUI environment for constructing and optimizing feed-forward assemblies from data files provided in standard formats.  Neuromorphic assemblies generated by the SL platform may be integrated into applications through OLE/OCX interfaces or directly through the HNeT API.

The HNet Application Programming Interface (API) allows developers to allocate specific cell types, and interconnect these cells to form cerebellar, neo-cortical, spatial-temporal, hyperincursive assemblies, or any combination / hybrid of these primary models.  The developer is able to independently modify any property associated with each cell, such as memory decay, learning rate, neural plasticity, I/O conversion operations, execution sequence, and a host of others.  The HNet API has been optimized for SIMD and hyper-threaded operation.  Cell assemblies may be structured to form supervised learning, unsupervised learning, spatial-temporal, and other more sophisticated models.