Background

A computer can process large amounts of data with great precision and speed. However, compared to the human brain, it still fails to achieve comparable performance in terms of cognitive functions such as perception, recognition and memory. For example, it is easy for a person to recognize the faces of famous people, read documents or communicate with the environment, but for a computer it is an extremely difficult task. The mechanisms in charge of this in the human brain remain unclear.

The application of biologically inspired methods in design and control has a long tradition in robotics. Artificial neural networks are one of the ways to transform the cognitive functions of the human brain into a computer form. Theoretical analysis of these artificial neural networks could offer the right way to explain the biological mechanisms behind the cognitive functions of our brain.

Neuromorphic computing is a very attractive field for the development of the future high-performance and intelligent computers. The computer is undoubtedly one of the greatest inventions in history. Based on computers, we have created a digital universe in which we can connect and communicate with each other anytime and anywhere. Over the last half-century, the decisive driving force in computer development has been the scaling of CPU and memory, the two main components in computers based on the von Neumann architecture. However, separate CPU and memory result in low efficiency, and scaling is already reaching its limits in this case. It's time to find a new solution and that is Neuromorphic Computing.