000 04010nam a22004815i 4500
001 978-1-4614-0724-9
003 DE-He213
005 20140220083240.0
007 cr nn 008mamaa
008 110914s2012 xxu| s |||| 0|eng d
020 _a9781461407249
_9978-1-4614-0724-9
024 7 _a10.1007/978-1-4614-0724-9
_2doi
050 4 _aRC321-580
072 7 _aPSAN
_2bicssc
072 7 _aMED057000
_2bisacsh
082 0 4 _a612.8
_223
100 1 _aRao, A. Ravishankar.
_eeditor.
245 1 4 _aThe Relevance of the Time Domain to Neural Network Models
_h[electronic resource] /
_cedited by A. Ravishankar Rao, Guillermo A. Cecchi.
264 1 _aBoston, MA :
_bSpringer US,
_c2012.
300 _aXVIII, 226 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Cognitive and Neural Systems ;
_v3
520 _aA significant amount of effort in neural modeling is directed towards understanding the representation of external objects in the brain. There is also a rapidly growing interest in modeling the intrinsically-generated activity in the brain, as represented by the default mode network hypothesis, and the emergent behavior that gives rise to critical phenomena such as neural avalanches. Time plays a critical role in these intended modeling domains, from the exquisite discriminations in the mammalian auditory system to the precise timing involved in high-end activities such as competitive sports or professional music performance. The growth in experimental high-throughput neuroscience techniques has allowed the multi-scale acquisition of neural signals, from individual electrode recordings to whole-brain functional magnetic resonance imaging activity, including the ability to manipulate neural signals with optogenetic approaches. This has created a deluge of experimental data, spanning multiple spatial and temporal scales, and posing the enormous challenge of its interpretation in terms of a predictive theory of brain function. In addition, there has been a massive growth in availability of computational power through parallel computing. The Relevance of the Time Domain to Neural Network Models aims to develop a unified view of how the time domain can be effectively employed in neural network models. The book proposes that conceptual models of neural interaction are required in order to understand the data being collected. Simultaneously, these proposed models can be used to form hypotheses of neural interaction and system behavior that can be neuroscientifically tested. The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems. This book will appeal to a wide audience consisting of computer scientists and electrical engineers interested in brain-like computational mechanisms, computer architects exploring the development of high-performance computing systems to support these computations, neuroscientists probing the neural code and signaling mechanisms, mathematicians and physicists interested in modeling complex biological phenomena, and graduate students in all these disciplines who are searching for challenging research questions.
650 0 _aMedicine.
650 0 _aNeurosciences.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 1 4 _aBiomedicine.
650 2 4 _aNeurosciences.
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aCecchi, Guillermo A.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461407232
830 0 _aSpringer Series in Cognitive and Neural Systems ;
_v3
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-0724-9
912 _aZDB-2-SBL
999 _c100912
_d100912