Neuromorphic computing, as a novel approach to processing information by mimicking biological neural networks, has gradually demonstrated significant ...

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 ...

Using machine learning and math, a BYU student improved a key tool firefighters rely on during wildfire season ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

A common objective for neural networks is to find a mathematical function, or curve, that best connects certain data points. The closer the network can get to that function, the better its predictions ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher ...

This is because neural networks require extensive training for their inputs (such as pixels in an image) to produce the appropriate output (such as a description of the image). For the training ...