Abu Sebastian, Manuel Le Gallo, et al.
Nature Nanotechnology
Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are based on memoryless models of neurons. This is unlike the neurons in the human brain that encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine-learning systems.
Abu Sebastian, Manuel Le Gallo, et al.
Nature Nanotechnology
Geoffrey W. Burr, Robert M. Shelby, et al.
Advances in Physics: X
Naigang Wang, Eugene J. O'Sullivan, et al.
Journal of Applied Physics
Benedikt Kersting, Vladimir Ovuka, et al.
Scientific Reports