Neural Learning

Neural Learning

Neural learning is based on the belief that the brain operates like a computer when it is 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 developing better learning and memory retention concepts. The framework also serves as the basis of artificial neural network systems.
According to the neural learning model, information first enters the brain through data input. The brain must then store this information and combine it with already present information via data organization. The final step is data retrieval, in which the brain develops systems for taking stored information from the mind and using it. Neural learning thus refers to these collective processes in which the brain gathers, stores, and uses information gained through life experiences. Sometimes, learning processes becomes so encoded in the brain that information retrieval occurs almost automatically, as in threatening situations.
Memory is therefore a vital concept in neural learning, just as it is with computers. Effective encoding of information can be aided with mnemonic techniques. These methods involve memorizing large chunks of information via memory cues. For example, an individual might seek to learn a long string of words by creating 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 commonplace in memorizing complex information like medical terms.
Mnemonic devices are often dependent on another important concept in neural learning: the type of learning style a brain is most wired to implement. Some individuals are more proficient with visual learning methods, while others work better when learning is more reading or word-based. Other approaches might include auditory learning and applied cooperative learning.
Some teachers of neural learning embrace a holistic approach to learning. In other words, individuals should consider ideas and concepts in a naturalistic way, rather than relying on rote learning methods that emphasize specific and isolated facts. Note-taking might thus consist of a tree-like approach in which concepts branch out from each other and individuals create their own unique associations to solidify concepts in their memory.
Transmission and storage of information occurs among networks of neurons, or brain cells. Neural networks are also the basis of much artificial intelligence. In fact, neural learning sometimes refers to the methods of artificial intelligence design that mimic human neural structures. Such neural networks have proven useful in numerous complex machine performance arenas ranging from speech recognition to implementing controls for robots.
For these methods, the artificial small structures that are patterned after 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 artificial intelligence machines, input and output components are connected repeatedly so that associations are created within the artificial intelligence system. These formed associations constitute neural learning for the system, and — like human learning — the associations can be strengthened as they are encoded and memorized. The strengthening occurs via learning rules, or weighted measurements and mathematical neural algorithms.