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100   $a20140320d2014      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDirected Information Measures in Neuroscience /$fedited by Michael Wibral, Raul Vicente, Joseph T. Lizier
205   $a1st ed. 2014.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014.
215   $a1 online resource (XIV, 225 p. 51 illus., 8 illus. in color.) 
225 1 $aUnderstanding Complex Systems,$x1860-0832
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-642-54473-8 
327   $aPart I Introduction to Directed Information Measures -- Part II Information Transfer in Neural and Other Physiological Systems -- Part III Recent Advances in the Analysis of Information Processing.
330   $aAnalysis of information transfer has found rapid adoption in neuroscience, where a highly dynamic transfer of information continuously runs on top of the brain's slowly-changing anatomical connectivity. Measuring such transfer is crucial to understanding how flexible information routing and processing give rise to higher cognitive function. Directed Information Measures in Neuroscience reviews recent developments of concepts and tools for measuring information transfer, their application to neurophysiological recordings and analysis of interactions. Written by the most active researchers in the field the book discusses the state of the art, future prospects and challenges on the way to an efficient assessment of neuronal information transfer. Highlights include the theoretical quantification and practical estimation of information transfer, description of transfer locally in space and time, multivariate directed measures, information decomposition among a set of stimulus/responses variables, and the relation between interventional and observational causality. Applications to neural data sets and pointers to open source software highlight the usefulness of these measures in experimental neuroscience. With state-of-the-art mathematical developments, computational techniques, and applications to real data sets, this book will be of benefit to all graduate students and researchers interested in detecting and understanding the information transfer between components of complex systems.
410  0$aUnderstanding Complex Systems,$x1860-0832
606   $aComputational complexity
606   $aCoding theory
606   $aInformation theory
606   $aBiomedical engineering
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aCoding and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15041
606   $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X
615  0$aComputational complexity.
615  0$aCoding theory.
615  0$aInformation theory.
615  0$aBiomedical engineering.
615 14$aComplexity.
615 24$aCoding and Information Theory.
615 24$aBiomedical Engineering and Bioengineering.
676   $a620
702   $aWibral$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aVicente$b Raul$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aLizier$b Joseph T$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910299726003321
996   $aDirected Information Measures in Neuroscience$92215529
997   $aUNINA