Artificial Neural Networks Made from Memristors for Brain-Inspired Computing

Research in neuromorphic computing aims to emulate the architecture and properties of the brain to create a new generation of computers that are as small and frugal as a laptop but as powerful as a supercomputer.

While reading these lines, your brain is efficiently performing image recognition to identify letters and words, matching it with your learned vocabulary to understand the content. Modern computer chips operate in a similar manner, providing a platform for running what are called artificial neural networks.

These are virtual networks made of artificial neurons and synapses, which are trained to process information for specific tasks such as facial recognition to unlock smartphone displays or tumor detection from brain scans. However, compared to the brain, artificial neural networks run on conventional computers are extremely slow and consume large amounts of energy.

Researchers have now made a step towards the creation of a new type of artificial neural network made from a material that hosts electrically conductive structures that resemble a network of synapses and neurons. With these brain-like structures, the material itself could potentially perform cognitive tasks with high efficiency, using them in brain-inspired computing architectures called neuromorphic computing.

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Research in neuromorphic computing aims to emulate the architecture and properties of the brain to create a new generation of computers that are as small and frugal as a laptop but as powerful as a supercomputer.