Information Theory in Neuroscience
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,...
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| Huvudupphov: | , |
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| Materialtyp: | Online |
| Språk: | engelska |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Länkar: | 32597 |
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| _version_ | 1869527307793727488 |
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| author | Piasini, Eugenio Panzeri, Stefano |
| author_browse | Panzeri, Stefano Piasini, Eugenio |
| author_facet | Piasini, Eugenio Panzeri, Stefano |
| author_sort | Piasini, Eugenio |
| collection | Directory of Open Access Books |
| description | 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, as well as to the development of analytical techniques to crack the neural code—that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience. |
| format | Online |
| id | doab-20.500.12854ir-50225 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-502252023-12-20T18:40:39Z Information Theory in Neuroscience Piasini, Eugenio Panzeri, Stefano QA1-939 Q1-390 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 bic Book Industry Communication::P Mathematics & science 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, as well as to the development of analytical techniques to crack the neural code—that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience. 2021-02-11T16:13:59Z 2021-02-11T16:13:59Z 2019-03-21 15:50:41 2019 book 32597 9783038976646 https://directory.doabooks.org/handle/20.500.12854/50225 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1171 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-665-3 10.3390/books978-3-03897-665-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038976646 280 open access |
| spellingShingle | QA1-939 Q1-390 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 bic Book Industry Communication::P Mathematics & science Piasini, Eugenio Panzeri, Stefano Information Theory in Neuroscience |
| title | Information Theory in Neuroscience |
| title_full | Information Theory in Neuroscience |
| title_fullStr | Information Theory in Neuroscience |
| title_full_unstemmed | Information Theory in Neuroscience |
| title_short | Information Theory in Neuroscience |
| title_sort | information theory in neuroscience |
| topic | QA1-939 Q1-390 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 bic Book Industry Communication::P Mathematics & science |
| topic_facet | QA1-939 Q1-390 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 bic Book Industry Communication::P Mathematics & science |
| url | 32597 |
| work_keys_str_mv | AT piasinieugenio informationtheoryinneuroscience AT panzeristefano informationtheoryinneuroscience |