Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning.
Simulators
Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks. They focus on one or a limited number of specific types of neural networks. They are typically stand-alone and not intended to produce general neural networks that can be integrated in other software. Simulators usually have some form of built-in visualization to monitor the training process. Some simulators also visualize the physical structure of the neural network.
Research simulators
Historically, the most common type of neural network software was intended for researching neural network structures and algorithms. The primary purpose of this type of software is, through simulation, to gain a better understanding of the behavior and the properties of neural networks. Today in the study of artificial neural networks, simulators have largely been replaced by more general component based development environments as research platforms.
Commonly used artificial neural network simulators include the Stuttgart Neural Network Simulator (SNNS), and Emergent.
In the study of biological neural networks however, simulation software is still the only available approach. In such simulators the physical biological and chemical properties of neural tissue, as well as the electromagnetic impulses between the neurons are studied.
Commonly used biological network simulators include Neuron, GENESIS, NEST and Brian.
Data analysis simulators
Unlike the research simulators, data analysis simulators are intended for practical applications of artificial neural networks. Their primary focus is on data mining and forecasting. Data analysis simulators usually have some form of preprocessing capabilities. Unlike the more general development environments, data analysis simulators use a relatively simple static neural network that can be configured. A majority of the data analysis simulators on the market use backpropagating networks or self-organizing maps as their core. The advantage of this type of software is that it is relatively easy to use. Neural Designer is one example of a data analysis simulator.
Simulators for teaching neural network theory
When the Parallel Distributed Processing volumes
were released in 1986-87, they provided some relatively simple software. The original PDP software did not require any programming skills, which led to its adoption by a wide variety of researchers in diverse fields. The original PDP software was developed into a more powerful package called PDP++, which in turn has become an even more powerful platform called Emergent. With each development, the software has become more powerful, but also more daunting for use by beginners.
In 1997, the tLearn software was released to accompany a book. This was a return to the idea of providing a small, user-friendly, simulator that was designed with the novice in mind. tLearn allowed basic feed forward networks, along with simple recurrent networks, both of which can be trained by the simple back propagation algorithm. tLearn has not been updated since 1999.
In 2011, the Basic Prop simulator was released. Basic Prop is a self-contained application, distributed as a platform neutral JAR file, that provides much of the same simple functionality as tLearn.
Development environments
Development environments for neural networks differ from the software described above primarily on two accounts – they can be used to develop custom types of neural networks and they support deployment of the neural network outside the environment. In some cases they have advanced preprocessing, analysis and visualization capabilities.
Component based
A more modern type of development environments that are currently favored in both industrial and scientific use are based on a component based paradigm. The neural network is constructed by connecting adaptive filter components in a pipe filter flow. This allows for greater flexibility as custom networks can be built as well as custom components used by the network. In many cases this allows a combination of adaptive and non-adaptive components to work together. The data flow is controlled by a control system which is exchangeable as well as the adaptation algorithms. The other important feature is deployment capabilities.