What is the neural network classification?

The neuron network classification is a process that makes computers able to classify data using the motifs found in biological neural networks. The neural circuit is the most complex known circuit and is capable of more data processing - both in parallel and in series - than any computer existing since 2011, and one of the reasons why it is so strong is the adaptive ability of the nerve circuits. The connection, synapses and functional logical gates can strengthen and weaken on the basis of previous information and speed of nerve shooting. The same adaptation of the circuit to effectively integrate information and data processing for statistical classification can be used by incorporating these motifs of circuits and methods into computer design. The motifs found in nerve circuits differ from simple processing units to complex information integrusystems. Biological adaptive systems in neural networks change the way they process information based on previous information received. The same way as a personIt teaches to drown background noise, the artificial neural network can learn to weigh separate information differently and give more weight that the system has learned to call "important".

Computational models for classification of neural networks use the knowledge gained from the studio of naturally occurring neural processing capabilities, from units in circuits to the process through which the information is weighed. These motifs can then be better understood, lending further insight into the brain functioning, and re -creating the motif in Silico , which means that computers are used for greater statistical classification of the neuron network classification. The application of the neural network classification is far -reaching, but the progress made until 2011 was relatively small, mainly because of our study and understanding neural networks.

Information processing methods that include adaptive systems mimic PThe bending circuits in the brain, such as machine learning based on previous data parameters, allow scientists to process data in a unique and dynamic way. Some people say this is necessary because the accumulation of data in science is growing exponentially, and techniques to increase the amount of data received become more efficient. Many scientists believe that a narrow place in scientific discoveries will be assimilation and data processing itself. For statistical analysis, non -linear learning methods based on the machine have relied more.

Programmers can create artificial neural networks composed of artificial neurons through a computing model in a unit of processing Silico information, which has the ability to dynamically change its statistical analysis based on previous data evaluated. In principle, data processing data to classify a neuron network can allow scientists to create more powerful machines that are able to learn. A machine capable of dynamically modifying your earsIFICAL COUPLES BY INFORMATION is a powerful tool that helps scientists in problems trying to analyze a large amount of data.