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Project Goals

TENSAV: Emulator of Neural Structures for Artificial Vision

TENSAV is designed and optimized primarily for visual object recognition. However, it's intended to be adaptable to other types of sensory input (e.g., audio, tactile). In addition to image inputs, it has the capability to input sets of numerical values.

The plan is for TENSAV to be able to emulate complex neuronal networks on a desktop PC or workstation with acceptable training times. The conflicting requirements of high-performance, large network sizes and minimal memory usage necessitates algorithms and data structures that are very complex.

TENSAV can model neurons ranging from simple point cells up to complex variable-state models. Networks can have multiple layers of sources and nodes with many connectivity options. As is typical, training the network is done by presenting repeated examples over multiple epochs using Hebbian-style learning. The hope is that the unique capabilities of the program will allow the number of training epochs to be fewer than is typical for standard ANN programs.

Temporal Emulator of Neural Structures for Artificial Vision

TENSAV is the name for a suite of three large main programs and four smaller utility computer programs developed for machine learning vision applications.

The name describes the basic function of the program suite. However, TENSAV is not yet another "deep learning" simulator. The program uses learning, but is designed to have a combination of features and capabilities that are not available on any existing simulators.

The project at this point consists of complete basic program code for the programs. There is also a 1400+ page illustrated programmer's Design Document. Preliminary User Manuals have also been completed describing how to use the programs.

Development and testing is ongoing, but the basic operation of the programs has been tested and debugged sufficiently to declare them at "alpha" stage.

The About page gives a limited description of TENSAV and the Background page describes the motivations for pursuing it.