2 Division of Blood and Marrow Transplantation, Department of Medicine.
3Stanford Diabetes Research Center, and.
4Northern California Breakthrough T1D Center of Excellence, Stanford School of Medicine, Stanford, California, USA.
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Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.
For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilising that value for in-memory processing.
However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.
Controlling light at dimensions thousands of times smaller than the thickness of a human hair is one of the pillars of modern nanotechnology.
An international team led by the Quantum Nano-Optics Group of the University of Oviedo and the Nanomaterials and Nanotechnology Research Center (CINN/Principalty of Asturias-CSIC) has published a review article in Nature Nanotechnology detailing how to manipulate fundamental optical phenomena when light couples to matter in atomically thin materials.
The study focuses on polaritons, hybrid quasiparticles that emerge when light and matter interact intensely. By using low-symmetry materials, known as van der Waals materials, light ceases to propagate in a conventional way and instead travels along specific directions, a characteristic that gives rise to phenomena that challenge conventional optics.
“There was no existing theory in the ferroelectrics community that could explain these results,” Liu explained.
Keeping Chaos at Bay with Small Amounts of Energy
To unlock the advanced material’s performance and open up potential commercial applications, Haixue Yan, a reader in materials science and engineering from Queen Mary University of London, explored several different ideas. That search effort led him to Liu’s relatively new zentropy theory idea. According to a statement announcing the new approach, zentropy theory suggests that systems trend towards disorder “if no energy is applied to keep the chaos at bay.”