LEADER 03980nam 2201009z- 450 001 9910346856603321 005 20231214133353.0 010 $a3-03897-665-2 035 $a(CKB)4920000000095101 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/50225 035 $a(EXLCZ)994920000000095101 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInformation Theory in Neuroscience 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (280 p.) 311 $a3-03897-664-4 330 $aAs 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. 610 $asynergy 610 $aGibbs measures 610 $acategorical perception 610 $aentorhinal cortex 610 $aneural network 610 $aperceived similarity 610 $agraph theoretical analysis 610 $aorderness 610 $anavigation 610 $anetwork eigen-entropy 610 $aIsing model 610 $ahigher-order correlations 610 $adiscrimination 610 $ainformation theory 610 $arecursion 610 $agoodness 610 $aconsciousness 610 $aneuroscience 610 $afeedforward networks 610 $aspike train statistics 610 $adecoding 610 $aeigenvector centrality 610 $adiscrete Markov chains 610 $asubmodularity 610 $afree-energy principle 610 $ainfomax principle 610 $aneural information propagation 610 $aintegrated information 610 $amismatched decoding 610 $amaximum entropy principle 610 $aperceptual magnet 610 $agraph theory 610 $ainternal model hypothesis 610 $achannel capacity 610 $acomplex networks 610 $arepresentation 610 $alatching 610 $anoise correlations 610 $aindependent component analysis 610 $amutual information decomposition 610 $aconnectome 610 $aredundancy 610 $amutual information 610 $ainformation entropy production 610 $aunconscious inference 610 $ahippocampus 610 $aneural population coding 610 $aspike-time precision 610 $aneural coding 610 $amaximum entropy 610 $aneural code 610 $aPotts model 610 $apulse-gating 610 $afunctional connectome 610 $aintegrated information theory 610 $aminimum information partition 610 $abrain network 610 $aQueyranne?s algorithm 610 $aprincipal component analysis 700 $aPiasini$b Eugenio$4auth$01292375 702 $aPanzeri$b Stefano$4auth 906 $aBOOK 912 $a9910346856603321 996 $aInformation Theory in Neuroscience$93022229 997 $aUNINA