Emergent

Neural networks are computer programs that aim to simulate a human brain, by acting like neurons and connecting with each other the same way brain cells do. In addition, by using various types of algorithms, these networks can be trained to perform certain tasks and even learn by themselves, albeit in a limited measure.

Visualize your neural network project data in 3D

Emergent is a software utility that aims to make the neural network domain more accessible to the public and to people who are not that experienced with programming languages, hence they would need much more time to create such a construct from scratch. To that effect, the application brings a collection of projects containing rich amounts of data and parameters for various types of networks.
The interactive part is represented by the program's ability to represent the aforementioned data as a graphical model, which can be easily visualized and analyzed, without having to understand the underlying code bits. That is not to mean you do not need any neural network knowledge, since you still have to deal with domain-specific terms and other related notions.

Take advantage of various supported algorithms

Since the subject at hand encompasses a rather large field, it is only natural that there are many variations and interpretations of how to design and simulate a neural network, made available to you through the variety of different algorithms included with the application. Thus, you can make use of the back-propagation, constraint satisfaction and Leabra algorithms, while the symbolic ACT-R architecture is supported as well.
Furthermore, you can also enjoy a complete Newtonian physics simulator, which is bundled into the 3D-compatible interface. This allows you to experiment with various robotics simulations and realistic visual processing of images, although its uses can extend to many other fields and domains.

In conclusion

Since Emergent is used by a large number of scientists in various projects around the world, you can count on its ability to deliver the expected performance and simulation accuracy. It is, however, still very hard to use without the appropriate set of skills and the required neural network knowledge.