A neural network simulator is a kind of tech tool that attempts to analyze systems that mirror the activities of the human or animal brain. Artificial neural networks imitate groups of biological neurons, in attempts to use the kinds of biological engineering in human and animal brains for the development of new technologies. A neural network simulator may provide modeling or some sort of research prototype for an artificial neural network.
Generally, the neural network simulator is a resource for researchers who are engaged in figuring out how the neural network operates. A wide variety of tools will analyze the algorithms in a neural network, and the processes that scientists can observe in these highly complex networks. Different kinds of data gathering help the simulator to evaluate what’s going on inside a biological or artificial network.
To effectively show human operators how a neural network operates, neural network simulators most often include versatile visual interfaces that present data in a graphical way. Many of these have multiple windows that are labeled for easy identification of data modules or task bars. Simulators can include color-coded visuals that show users how a neural network is working in simulation.
The nature of a neural network simulator is that it tries to copy the workings of the network. Experts have pointed out that in today’s research world, the tools that researchers use to evaluate artificial neural networks are often more complex than a single simulation. For this reason, scientists who study artificial neural networks may refer to these tools as more general “platforms” or “research environments.”
Neural network simulators are still the most advanced way to evaluate biological neural networks. These tools are popular in observing the brain behavior of people and animals. Another class of simulators called data analysis simulators are often used for tasks like data mining and forecasting. Simulators can provide predictive models, or simply passively relay information about a test or network operation.
Another way that neural network simulators are differentiated is by the way that they generate or capture data. These include database technologies where a specific model may appeal to a research and development team, according to the parameters of their research and their intentions or eventual goals. These range from simple spreadsheet designs to complex multi-window programs with advanced algorithms and capacity.
