What If Computers Could Think Like The Human Brain?

Traditional computing revolves around fixed and rigid processes, an approach that will no longer be able to co-exist with our rapid development in computing in the 21st century. Neuromorphic computing takes an approach to contradict exactly this, using a system greatly familiar to us–the human brain. This newfound, contemporary architecture aims to completely mimic the structure and functioning of the brain with parallel components in the software or hardware that are now able to learn and adapt to any situation, using its complex networking system. Introducing this advanced form of computing has its respective, vital applications ranging from development in fields like robotics and computing to improving everyday lives in healthcare and transport.

How Does It Work?

Seeing that this approach is ultimately an imitation of the nervous system, it would include parallel components that biology already takes into consideration. Biology utilizes the components of neurons (building blocks of the nervous system) and synapses (gaps present between neurons) to process and communicate information throughout the body. The architecture in this case is widely intricate as the network consists of over a billion neurons all interacting with each other, having to send signals back and forth.

Similarly, neuromorphic computing utilizes items such as artificial neurons and synapses, which are respectively known as nodes and connections. The structure within the computer is rather similar to that of the brain, although the technology is implemented through certain hardware, mainly silicon chips. Neuromorphic computing attempts to enhance this already existing system through an idea known as a Spiking Neural Network (or SNN). Instead of being largely driven by calculations, these SNNs are event-driven, meaning that they have a more active way of learning over time. The information received here is in the form of spikes and the network is developed to adapt to past learnings.

The algorithms that are incorporated into Neuromorphic computing revolve around software, consisting of systems like “Deep Learning”, “Evolutionary Algorithms", and “Plasticity”. These algorithms use the existing concepts of SNNs and neuron spiking in order to develop a phenomenon that systematically solves a problem. One such example, an evolutionary algorithm, is designed in order to replicate certain principles centered in biology.

How Valuable Is It?

The immense value of Neuromorphic computing stems from its key principles, improvement from the old system and numerous applications. Originally, traditional computing revolved around the concept of the “von Neumann " architecture, one that had a system where the Central Processing Unit (CPU), regulates the processes, and the memory would store the data. This inefficient, unreliable and limited architecture created what is called a “bottleneck” due to the fact that the CPU and memory unit have to constantly exchange information.

Key principles of neuromorphic computing consist of its efficiency, parallel processing and real-time learning. Due to the fact that this form of computing is event-driven, the technology only consumes a certain amount of power for a period of time. The computing system allows for parallel processing, revolving around the idea of multitasking. Finally, the immense adaptability or “real-time learning” present in neuromorphic computing revolutionizes how we look at AI.

The applications of neuromorphic computing are essential, not only in the development of certain fields, but also an improvement in the quality of life through enhanced healthcare, autonomous vehicles and smart devices. In robotics, this approach is utilized when devices can increase sensory input processing speeds. Navigation becomes easier and safer when dealing with autonomous vehicles, where real time processing of data is present. Brain-machine interfaces (BMI’s) for prosthetics can now be advanced due to computing.

What Are Its Limitations?

Although its modern approach makes it seem credible and high-performing, certain technical, economical and social challenges do come into play. Technical aspects include the lack of benchmarks and standards in order for it to be implemented. Contrasting from traditional computing where specific benchmarks deal with speed and memory, there are no such benchmarks present for neuromorphic computing because of the difference in functioning. Currently, this concept is not one that has been widely implemented, but has worked admirably at a small scale.

The economic challenges neuromorphic computing imposes include the high additional costs and high adoption. The scalability or widespread adoption could limit the development of neuromorphic computing. Aside from the particular components and hardware, the costs of the manufacturing processes in which we develop our understanding are high. Here, market adoption becomes difficult due to the limited accessibility to smaller firms and startups.

Social challenges turn evident in this scenario leading to an increase in job disruption and displacement, along with widening the digital divide. Even though this form of computing is seen as advanced and beneficial to society, it is only expanding the use of automation in modern technology, insinuating for a dystopian society.

While the complexity and intricacy of neuromorphic computing might seem fascinating, the introduction of such technology poses significant challenges, whether technical, economical or social. If computers start to mimic the human brain, would we be able to solve the toughest problems or will nothing truly change?

Bibliography:

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