Neural network

A neural network is either a biological neural network, made up of biological neurons, or an artificial neural network (ANN), for solving artificial intelligence (AI) problems.

A biological neural network in a biological brain is composed of a vast network of chemically connected or functionally associated neurons. Connections are bio-electrical and signals are sent within and out of the network via neurotransmitters.

Historically, digital computers evolved from the von Neumann model, and operate via the execution of explicit instructions via access to memory by a number of processors. Unlike this model, neural network computing does not separate memory and processing.

An ANN is an interconnected group of artificial neurons that uses software with an adaptive and connectionist approach. They are used to model complex relationships between inputs and outputs or to find patterns in data. This complex global behavior is suited for deep learning algorithms and AI.

Applications include:


 * Software in computer and video games or autonomous robots.
 * Time series prediction and modeling.
 * Data processing and compression algorithms
 * Game-playing and decision making (chess)
 * Optical character recognition
 * Pattern and image recognition (radar systems, face identification / Facebook, object recognition / google lens)
 * Sequence recognition (gesture, speech, handwritten text recognition)


 * Robotics, including directing manipulators and prostheses.


 * Cybersecurity and identifying malware and virus heuristics

In 2014, bioengineers at Stanford University developed a circuit modeled on the human brain. Neurogrid is designed specifically for simulation of biological brains. Its board contains sixteen Neurocores, each of which has 256 x 256 silicon neurons in a 12 x 14 mm chip. It is claimed to be 9,000 times faster as well as more energy efficient than a typical PC.