000 | 03879nam a22005175i 4500 | ||
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001 | 978-3-319-68297-6 | ||
003 | DE-He213 | ||
005 | 20181204134421.0 | ||
007 | cr nn 008mamaa | ||
008 | 180320s2017 gw | s |||| 0|eng d | ||
020 |
_a9783319682976 _9978-3-319-68297-6 |
||
024 | 7 |
_a10.1007/978-3-319-68297-6 _2doi |
|
040 | _aISI Library, Kolkata | ||
050 | 4 | _aT57-57.97 | |
072 | 7 |
_aPBW _2bicssc |
|
072 | 7 |
_aMAT003000 _2bisacsh |
|
072 | 7 |
_aPBW _2thema |
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082 | 0 | 4 |
_a519 _223 |
245 | 1 | 0 |
_aMathematical and Theoretical Neuroscience _h[electronic resource] : _bCell, Network and Data Analysis / _cedited by Giovanni Naldi, Thierry Nieus. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
|
300 |
_aIX, 253 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSpringer INdAM Series, _x2281-518X ; _v24 |
|
505 | 0 | _a1 Simulating cortical Local Field Potentials and Thalamus dynamic regimes with integrate-and-fire neurons -- 2 Computational modeling as a means to defining neuronal spike pattern behaviors -- 3 Chemotactic guidance of growth cones: a hybrid computational model -- 4 Mathematical Modeling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions -- 5 Bifurcation analysis of a sparse neural network with cubic topology -- 6 Simultaneous jumps in interacting particle systems: from neuronal networks to a general framework -- 7 Neural fields: Localised states with piece-wise constant interactions -- 8 Mathematical models of visual perception based on cortical architectures -- 9 Mathematical models of visual perception for the analysis of Geometrical optical illusions -- 10 Exergaming for autonomous rehabilitation -- 11 E-infrastructures for neuroscientists: the GAAIN and neuGRID examples -- 12 Nonlinear Time series Analysis -- 13 Measures of spike train synchrony and Directionality -- 14 Space-by-time tensor decomposition of single-trial analysis of neural signals -- 15 Inverse Modeling for MEG/EEG data. | |
520 | _aThis volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical and numerical topics; statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience. | ||
650 | 0 | _aMathematics. | |
650 | 0 |
_aBiology _xData processing. |
|
650 | 0 | _aStatistics. | |
650 | 1 | 4 |
_aApplications of Mathematics. _0http://scigraph.springernature.com/things/product-market-codes/M13003 |
650 | 2 | 4 |
_aComputer Appl. in Life Sciences. _0http://scigraph.springernature.com/things/product-market-codes/L17004 |
650 | 2 | 4 |
_aStatistics for Life Sciences, Medicine, Health Sciences. _0http://scigraph.springernature.com/things/product-market-codes/S17030 |
700 | 1 |
_aNaldi, Giovanni. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aNieus, Thierry. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319682969 |
776 | 0 | 8 |
_iPrinted edition: _z9783319682983 |
830 | 0 |
_aSpringer INdAM Series, _x2281-518X ; _v24 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-68297-6 |
912 | _aZDB-2-SMA | ||
942 | _cEB | ||
950 | _aMathematics and Statistics (Springer-11649) | ||
999 |
_c427257 _d427257 |