Post-traumatic stress symptoms in patients with chronic low back pain were associated with altered prefrontal engagement and increased coupling with subcortical threat-processing circuits during ...

Abstract: The spike neuron simulates the dynamic model of biological neuron, and it delivers spikes according to the changes of membrane potential. Therefore, the spiking neural networks (SNNs) ...

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...

In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method that aligns memristor hardware's noisy updates with neural network training, ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Abstract: A vectorized version of the back propagation algorithm for fully connected artificial neural networks was implemented in order to provide a practical analysis of its convergence. Several ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...