Modeling Brain Function

Graph neural networks (GNNs) have achieved state-of-the- art performance in graph representation learning by exploit- ing statistical correlations. However, ... May 9, 2023 ... The article examines the role of the spontaneous activity of neurons, and therefore, of brain neural networks, in information processing. Sat 7:10 a.m. - 7:18 a.m.. Neural networks learn an environment's geometry in latent space by performing predictive coding on visual scenes ( Oral ) > link Feb 26, 2024 ... This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more ... Jan 10, 2024 ... (2007, 2010), for instance, used a network composed of two coupled rings, representing sensory and frontal cortical modules, to model the ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ... ... information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information ... Jun 29, 2022 ... Neural Networks are one particular type of Machine Learning technique. They are a type of artificial intelligence modeled on the brain. Feb 7, 2018 ... ... information than excitatory connections, known to enhance entropy in the network. ... Neural information · Neuronal dynamics · Topological tools ... Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ...