A neural algorithm commonly refers to a piece of code used in neural programming. This is where a neural network simulates specific behaviors and attributes of the human brain. Programmers talk about neural programming as a process evolved from older systems, where today's neural programming community builds on principles of artificial intelligence presented decades ago.
The neural algorithm is a specific part of neural systems that helps facilitate one of the greater roles of neural software. It often provides for combining different data for a specialized result, where the neural algorithm fills in the gaps much like a human cerebral process would do, for example, in a limited range of vision. In artificial neural programming, this is done by projecting from known data to present a likely result.
Many neural algorithm setups involve taking a known input, and adding another kind of "training data" to get a final result that combines both. Developers look closely at machine learning to define how well their neural algorithms are producing the capability of a computer program to learn. Beyond this, there are a wide range of types of neural algorithms intended for different goals and implemented in different ways.
Programmers often include detailed diagrams to show how each component of a neural algorithm blends into the mix. These may be published in print or on the web to help a public developer community interpret what a single programmer or team has done with a neural algorithm to enhance a piece of software. Like all programming, neural algorithm development relies heavily on conventional language and coding, standard documentation practices, and clarity from the original team to make the result accessible to a wider audience. Without this, it becomes difficult to translate the original intent and functionality of an algorithm or program.
Along with fundamental roles in fields like logistics and observational sciences, neural applications have now become popular in unlikely places. One of these is in horse racing, where developers of computer programs now claim that neural algorithms can be used to effectively predict outcomes. Although these kinds of uses are similar to other common practices for neural software design, it's debatable how well neural applications can predict a particular event. The interest in using a neural algorithm design to track data-rich events like stock market changes is big enough to ensure that neural programming will be a big part of future efforts to develop computer programs that help human operators in specific predictive ways.
