What Is a Convolutional Neural Network?

A convolutional neural network is a certain type of arrangement of artificial neurons, or neuron simulators, that is made to function in a particular way. Neural networks are biological groups of neurons, or artificial groups of pseudo-neurons that are programmed to work in the same way as biological neurons. Artificial neural networks seek to imitate functions of the human or animal brain.

In most cases, a convolutional neural network is simply an artificial neural network made to simulate some sort of brain activity. Experts call these models “biologically inspired.” Some of these may also be able to learn in the way that a biological neural network learns, through processing information in very complex ways.

Among the most widespread uses of convolutional neural networks is the simulation of human or animal vision. These applications often focus on the combination of input and output that help the technology do artificially what a brain does naturally. Many complex methods, sometimes called layers, are needed to achieve this sort of simulation. These are often displayed through visual models that help readers understand how a convolutional neural network is set up.

In general, scientists who implement convolutional neural networks have figured out some of the specific ways that brains process images. Artificial intelligence has progressed in recent times, and now scientists can make technologies perform some of the tasks that used to be exclusive to biological vision. One of these is facial recognition, where advanced algorithms allow cameras and other devices to effectively screen images and recognize an individual face.

Many types of convolutional neural network models are made to recognize different features in order to analyze an entire image that would simulate a range of vision. Some of these technologies also have to have advanced filters for certain ranges of light, or other tools that help technologies to “see” in the ways that humans and animals do. Convolutional neural networks need to be rigorously tested and assessed on their merits, where the specific achievement of results proves that these technologies can imitate the human or animal brain, at least to some degree.

Convolutional neural networks are also made for various different applications. These include consumer product uses such as facial recognition cameras. There are also many security applications for these kinds of technologies, and the use of convolutional neural networks as a powerful data sifting resource. Scientists continue to work on achieving more complexity with these simulators, for example, into checking numbers of faces in an image, or in being able to correctly identify faces in different scales, lighting, or other conditions.