Herein, we develop a programmable energy-efficient hardware implementation for Recurrent Neural Networks (RNNs) with Resistive Random-Access Memory (ReRAM) synapses and ultra-low power, area-efficient ...

Underwater imaging presents unique challenges due to light absorption, scattering, and color distortion. Conventional image enhancement methods often fail to address these issues adequately, resulting ...

A biphasic structural plasticity rule interacts with homeostatic synaptic scaling to maintain firing rate homeostasis in neural networks.

This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

Researchers have developed a groundbreaking 3D brain model that closely mirrors the architecture and function of the human brain.

A new technical paper titled “Machine Intelligence on Wireless Edge Networks” was published by researchers at MIT and Duke ...

This change is used as an output signal, so such structures work like artificial neurons in a neural network," said Alexey Kavokin. The architecture proposed by St. Petersburg State University ...

Additionally, recent pilot-assisted receiver designs have integrated data-driven neural network architectures to jointly perform channel estimation and signal detection in multi-user MIMO systems ...

This architecture has a split between on-premises and cloud resources, with some components remaining on-premises while others are moved to the cloud. Multicloud rebinding with cloud brokerage ...

Terabytes per second of bandwidth, miles of copper cabling, all crammed into the back of a single rack Analysis If you ...