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Neural learning is based on the idea that the brain functions like a computer, with data input, organization, and retrieval. Memory and learning styles are important concepts, and mnemonic techniques can aid effective coding of information. Neural networks are the basis of artificial intelligence, with units or nodes receiving and transmitting information to create associations within the system. Reinforcement occurs via learning rules and mathematical algorithms.
Neural learning is based on the belief that the brain operates like a computer when processing new information. Data input, organization, and retrieval are primary considerations. The biological basis of neural learning is a neural system, which refers to the interconnected structure of brain cells. This understanding of the relationship between brain structure and function has been applied to the development of improved learning and memory retention concepts. The framework also serves as the foundation for artificial neural network systems.
According to the neural learning model, information first enters the brain through data input. The brain then has to memorize this information and combine it with information already present through data organization. The final step is data retrieval, in which the brain develops systems for taking stored information from the mind and putting it to use. Neural learning therefore refers to these collective processes in which the brain collects, stores and uses information acquired through life experiences. Sometimes, learning processes become so encoded in the brain that information retrieval occurs almost automatically, such as in threatening situations.
Memory is therefore a vital concept in neural learning, just as it is with computers. Effective coding of information can be aided with mnemonic techniques. These methods involve storing large chunks of information via memory cues. For example, an individual might try to learn a long string of words by building a sentence in which each word contains the first letter of every word in the list. Another approach might involve creating an imaginative visual image that represents a word. This approach is common in memorizing complex information such as medical terms.
Mnemonic devices often depend on another important concept in neural learning: the type of learning style a brain is most likely to implement. Some individuals are more proficient with visual learning methods, while others do better when the learning is more reading- or word-based. Other approaches might include auditory learning and applied cooperative learning.
Some neural learning teachers embrace a holistic approach to learning. In other words, individuals should consider ideas and concepts naturalistically, rather than relying on rote learning methods that emphasize specific, isolated facts. Note-taking could therefore consist of a tree approach where concepts branch off from each other and individuals create their own unique associations to consolidate concepts in their memory.
The transmission and storage of information occurs between networks of neurons or brain cells. Neural networks are also the basis of much artificial intelligence. In fact, neural learning sometimes refers to AI design methods that mimic human neural structures. Such neural networks have proven useful in numerous arenas of complex machine performance ranging from speech recognition to the implementation of controls for robots.
For these methods, the small artificial structures modeled on human neurons are known as units or nodes. Like neurons, these units are programmed to receive incoming information, or input, and also to transmit information, or output. In AI machines, the input and output components are linked repeatedly so as to create associations within the AI system. These formed associations constitute the neural learning for the system, and like human learning, the associations can be strengthened as they are encoded and stored. Reinforcement occurs via learning rules or weighted measurements and mathematical neural algorithms.
