000 03443nam a22005175i 4500
001 978-3-642-54474-3
003 DE-He213
005 20200420220214.0
007 cr nn 008mamaa
008 140320s2014 gw | s |||| 0|eng d
020 _a9783642544743
_9978-3-642-54474-3
024 7 _a10.1007/978-3-642-54474-3
_2doi
050 4 _aQA76.9.M35
072 7 _aGPFC
_2bicssc
072 7 _aTEC000000
_2bisacsh
082 0 4 _a620
_223
245 1 0 _aDirected Information Measures in Neuroscience
_h[electronic resource] /
_cedited by Michael Wibral, Raul Vicente, Joseph T. Lizier.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXIV, 225 p. 51 illus., 8 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnderstanding Complex Systems,
_x1860-0832
505 0 _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.
520 _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.
650 0 _aEngineering.
650 0 _aCoding theory.
650 0 _aComplexity, Computational.
650 0 _aBiomedical engineering.
650 1 4 _aEngineering.
650 2 4 _aComplexity.
650 2 4 _aCoding and Information Theory.
650 2 4 _aBiomedical Engineering.
700 1 _aWibral, Michael.
_eeditor.
700 1 _aVicente, Raul.
_eeditor.
700 1 _aLizier, Joseph T.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642544736
830 0 _aUnderstanding Complex Systems,
_x1860-0832
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-54474-3
912 _aZDB-2-ENG
942 _cEBK
999 _c51509
_d51509