NEUROMORPHIC COMPUTING

Neuromorphic computing is a method of computer engineering in which elements of a computer are modeled after systems in the human brain and nervous system. The term refers to the design of both hardware and software computing elements. Neuromorphic computing is sometimes referred to as neuromorphic engineering.

Neuromorphic engineers draw from several disciplines -- including computer science, biology, mathematics, electronic engineering and physics -- to create bio-inspired computer systems and hardware. Of the brain's biological structures, neuromorphic architectures are most often modelled after neurons and synapses. This is because neuroscientists consider neurons the fundamental units of the brain.

Neurons use chemical and electronic impulses to send information between different regions of the brain and the rest of the nervous system. Neurons use synapses to connect to one another. Neurons and synapses are far more versatile, adaptable and energy-efficient information processors than traditional computer systems.

Neuromorphic technology is expected to be used in the following ways:

• Deep learning applications;
• Next-generation semiconductors;
• Transistors;
• Accelerators; and
• Autonomous systems, such as robotics, drones, self-driving cars and artificial intelligence (AI).

Neuromorphic computing uses hardware based on the structures, processes and capacities of neurons and synapses in biological brains. The most common form of neuromorphic hardware is the spiking neural network (SNN). In this hardware, nodes -- or spiking neurons -- process and hold data like biological neurons.

Artificial synaptic devices connect spiking neurons. These devices use analog circuitry to transfer electrical signals that mimic brain signals. Instead of encoding data through a binary system like most standard computers, spiking neurons measure and encode the discrete analog signal changes?themselves.