Information Theory in Neuroscience

• Autor principal: Piasini, Eugenio (auth)
• Outros Autores: Panzeri, Stefano (auth)
• Formato: Recurso Electrónico Capítulo de Livro
• Idioma: inglês
• Publicado em: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Assuntos: synergy; Gibbs measures; categorical perception; entorhinal cortex; neural network; perceived similarity; graph theoretical analysis; orderness; navigation; network eigen-entropy; Ising model; higher-order correlations; discrimination; information theory; recursion; goodness; consciousness; neuroscience; feedforward networks; spike train statistics; decoding; eigenvector centrality; discrete Markov chains; submodularity; free-energy principle; infomax principle; neural information propagation; integrated information; mismatched decoding; maximum entropy principle; perceptual magnet; graph theory; internal model hypothesis; channel capacity; complex networks; representation; latching; noise correlations; independent component analysis; mutual information decomposition; connectome; redundancy; mutual information; information entropy production; unconscious inference; hippocampus; neural population coding; spike-time precision; neural coding; maximum entropy; neural code; Potts model; pulse-gating; functional connectome; integrated information theory; minimum information partition; brain network; Queyranne's algorithm; principal component analysis
• Acesso em linha: DOAB: download the publication; DOAB: description of the publication

As the ultimate information processing device, the brain naturally lends itself to being studied with information theory. The application of information theory to neuroscience has spurred the development of principled theories of brain function, and has led to advances in the study of consciousness,...