Neuromorphic Computing

• Neuromorphic computing is a concept developed by Carver Mead  in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.

• In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). 

• The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors.

• A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change.

• Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.

• Neuromorphic computing utilizes an engineering approach or method based on the activity of the biological brain.

• This type of approach can make technologies more versatile and adaptable, and promote more vibrant results than other types of traditional architectures, for instance, the von Neumann architecture that is so useful in traditional hardware design.

• Neuromorphic computing has been around for a while, but it is now beginning to be applied in new and different ways. A prime example is the proposal to create neuromorphic chips which are more complex in nature than traditional microprocessors.

• Neuromorphic chips would have architectures more like the neurons of the human brain, allowing them to process information in more specialized ways.

              
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