Using the analogy of the interconnected neural pathways in the brain, network leadership is about harnessing connections to ...

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 ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Psilocybin, a psychedelic compound contained in some varieties of mushrooms, has recently been found to be promising for the ...

Binunya, F. and Zhou, H. (2025) Multilingual Text Recognition and Assistance for Low-Resource Languages Using Computer Vision. Open Access Library Journal, 12, 1-20. doi: 10.4236/oalib.1113574 .

Researchers have developed a transfer learning-enhanced physics-informed neural network (TLE-PINN) for predicting melt pool morphology in selective laser melting (SLM). This novel approach ...

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. These layers include an input, multiple hidden, and output ...

Deconvolutional neural networks simply work in reverse of convolutional neural networks. The application of the network is to detect items that might have been recognized as important under a ...

Recurrent neural networks, or RNNs, are a style of neural network that involve data moving backward among layers. This style of neural network is also known as a cyclical graph.