Examines the function approximation properties of the "random neural-network model" or GNN, The output of the GNN can be computed from the firing probabilities of selected neurons. We consider a ...

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 ...

The brains of humans and other primates are known to execute various sophisticated functions, one of which is the ...

Two RIKEN researchers have used a scheme for simplifying data to mimic how the brain of a fruit fly reduces the complexity of ...

Microsoft is finally equipping the Snipping Tool with an export function for animated GIFs. This means that animated screen ...

Various approximation algorithms exist, but they often lack efficiency or accuracy. We introduce TGNN-Bet, a temporal graph neural network model, to approximate temporal betweenness centrality. In ...

This work introduces NEON (Neural Epistemic Operator Networks), a novel architecture for uncertainty-aware operator learning that enables epistemic uncertainty quantification using a single model, ...

Network Meta-Analysis of 4 Rehabilitation Methods With rTMS on Upper Limb Function and Daily Activities in Patients ... The combination of smart rehabilitation therapy with rTMS had the most ...

Neuroscience research. Learn how the brain's physical, chemical and electrical structure can affect everything from motivation and sensory perception to disease recovery.