The basic idea of neuromorphic computing is to create a chip that emulates the brain's functionality. By rethinking computing, scientists are applying the latest understanding of neuroscience and intersecting it with computer science.
This has brought about advancements in various fields like cognitive science, motor neuroprosthetics, etc.
Computers have been built to mimic the brain for decades. For instance, Frank Rosenblatt developed the Perceptron algorithm in 1958, a type of artificial neural network designed for simple pattern recognition tasks. The Perceptron was inspired by how the brain's neurons process information.
Neuromorphic computing systems are designed to replicate the structure and functionality of the brain to perform various tasks effectively.
In this article, we'll explain what neuromorphic computing is and why it is gaining traction across different applications.
What is Neuromorphic Computing?
Neuromorphic computing was inspired by a biological phenomenon called Synaptic Plasticity which is our brain's ability to change and adapt in response to new information.
The Neuron cells in the brain are known as the messengers. They are connected through synapses which are the junction points that link the neurons together, through which impulses and chemical signals are transmitted.
Neurons communicate with each other using electrical impulses called spikes. Spikes and synapses are essential in how the nervous system responds to mental activities.
When computing large data, traditional computers require large memory, this means the more the computation, the more memory capacity is required. Unlike the brain whose memory is created through new and strengthened connection between neurons by a process called synaptic plasticity.
The process of learning a new skill for instance can strengthen certain synapses and build long term memory in the brain. This is the one of the key reasons why the brain is a model for creating neurocomputing devices, its ability to adapt to changes, amongst many other reasons which we’ll see as the article progresses.
First, let us break Neuromorphic Computing into two parts to understand the concept separately before defining them as a whole.
Neuromorphic is defined as any large-scale system of integrated circuits that mimics neuro-biological architectures present in the nervous system; in other words, a system designed using the structure and function of the human brain.
Neuro: refers to the neurons or the nervous system.
Morphic: refers to a form or structure.
Computing is defined as the process of using computers to perform various tasks or solve problems using algorithms and software.
The conventional CPU is designed differently from the way the brain works. Ninety-nine percent of conventional chips are synchronous in design, making them rigid and sequential.
Neuromorphic computing, however, represents the latest in neuroscience and computer architecture. Its asynchronous nature is a completely different approach from the norm.
Neuromorphic computing is inspired completely by the brain's functions. It involves designing and engineering computers to mimic the neural architecture and functionality of the human brain. With neuromorphic chips, scientists are creating a vast network of neurons without any prescribed order, resulting in much more flexible functionality.
The brain is typically more efficient in terms of energy consumption, parallel processing, and so forth. Unlike traditional computers, it can perform several tasks without expending too much energy, where more data requires more computing power.
Neuromorphic computing seeks to leverage the brain's architecture to create more powerful and efficient systems for different tasks like decision-making, adaptive learning, pattern recognition, and more.
