000 | 03566nam a22006015i 4500 | ||
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001 | 978-981-287-874-8 | ||
003 | DE-He213 | ||
005 | 20220801222322.0 | ||
007 | cr nn 008mamaa | ||
008 | 160204s2016 si | s |||| 0|eng d | ||
020 |
_a9789812878748 _9978-981-287-874-8 |
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024 | 7 |
_a10.1007/978-981-287-874-8 _2doi |
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072 | 7 |
_aUYQ _2bicssc |
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_a006.3 _223 |
100 | 1 |
_aPeterson, James K. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _960854 |
|
245 | 1 | 0 |
_aCalculus for Cognitive Scientists _h[electronic resource] : _bDerivatives, Integrals and Models / _cby James K. Peterson. |
250 | _a1st ed. 2016. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2016. |
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300 |
_aXXXI, 507 p. 105 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aCognitive Science and Technology, _x2195-3996 |
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505 | 0 | _aIntroductory Remarks -- Viability Selection -- Limits and Basic Smoothness -- Continuity and Derivatives -- Sin, Cos and All That -- Antiderivatives -- Substitutions -- Riemann Integration -- The Logarithm and Its Inverse -- Exponential and Logarithm Function Properties -- Simple Rate Equations -- Simple Protein Models -- Logistics Models -- Function Approximation -- Extreme Values -- Numerical Methods Order One ODEs -- Advanced Protein Models -- Matrices and Vectors -- A Cancer Model -- First Order Multivariable Calculus -- Second Order Multivariable Calculus -- Hamilton’s Rule In Evolutionary Biology -- Final Thoughts -- Background Reading. | |
520 | _aThis book provides a self-study program on how mathematics, computer science and science can be usefully and seamlessly intertwined. Learning to use ideas from mathematics and computation is essential for understanding approaches to cognitive and biological science. As such the book covers calculus on one variable and two variables and works through a number of interesting first-order ODE models. It clearly uses MatLab in computational exercises where the models cannot be solved by hand, and also helps readers to understand that approximations cause errors – a fact that must always be kept in mind. | ||
650 | 0 |
_aComputational intelligence. _97716 |
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650 | 0 |
_aNeural networks (Computer science) . _960855 |
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650 | 0 |
_aMathematical physics. _911013 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aImage processing—Digital techniques. _931565 |
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650 | 0 |
_aComputer vision. _960856 |
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650 | 1 | 4 |
_aComputational Intelligence. _97716 |
650 | 2 | 4 |
_aMathematical Models of Cognitive Processes and Neural Networks. _932913 |
650 | 2 | 4 |
_aTheoretical, Mathematical and Computational Physics. _931560 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569 |
710 | 2 |
_aSpringerLink (Online service) _960857 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789812878724 |
776 | 0 | 8 |
_iPrinted edition: _z9789812878731 |
776 | 0 | 8 |
_iPrinted edition: _z9789811357190 |
830 | 0 |
_aCognitive Science and Technology, _x2195-3996 _960858 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-287-874-8 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
942 | _cEBK | ||
999 |
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