By mimicking how the brain operates, neuromorphic computing can use dramatically less energy than conventional electronic AI ...

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation ...

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times ...

Designing materials that steer light is a slow kind of trial and error. Each candidate structure must be tested in computer ...

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve ...

A tandem neural network capable of inferring key physical parameters of semiconductor materials from simple transistor measurements has been developed, as reported by researchers from the Institute of ...

Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance. However, in new research I have used a phenomenon called “quantum tunnelling” to ...

(A–C) Representative images reconstructed by conventional method (left) and new method (right) of microtubules, nuclear pore complexes and F-actin samples. The regions enclosed by the white boxes are ...

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance ...

DANA POINT, Calif., April 2, 2026 /PRNewswire/ -- EvoChip.ai, a computer architecture innovator redefining AI efficiency, today announced results from a controlled benchmark study demonstrating that ...