Overfitting

Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure. Feb 7, 2018 ... ... information than excitatory connections, known to enhance entropy in the network. ... Neural information · Neuronal dynamics · Topological tools ... ... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... Sep 22, 2020 ... By recruiting adjacent neurons into oscillations, waves of synchronously firing neuronal networks—and thereby conscious thoughts—can travel ... In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... ... information is transformed into valuable information with neural net. The individual element of the neural network, i.e., the living neuronal cell, is ... Nov 2, 2004 ... ... neural system. ... Dehaene S, Sergent C, Changeux JP: A neuronal network model linking subjective reports and objective physiological data during ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process.