000 | 03413nam a22005535i 4500 | ||
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001 | 978-981-287-871-7 | ||
003 | DE-He213 | ||
005 | 20200421111202.0 | ||
007 | cr nn 008mamaa | ||
008 | 160210s2016 si | s |||| 0|eng d | ||
020 |
_a9789812878717 _9978-981-287-871-7 |
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024 | 7 |
_a10.1007/978-981-287-871-7 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aPeterson, James K. _eauthor. |
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245 | 1 | 0 |
_aBioInformation Processing _h[electronic resource] : _bA Primer on Computational Cognitive Science / _cby James K. Peterson. |
250 | _a1st ed. 2016. | ||
264 | 1 |
_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2016. |
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300 |
_aXXXV, 570 p. 165 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, _x2195-3988 |
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505 | 0 | _aBioInformation Processing -- The Diffusion Equation -- Integral Transforms -- The Time Dependent Cable Solution -- Mammalian Neural Structure -- Abstracting Principles of Computation -- Abstracting Principles of Computation -- Second Messenger Diffusion Pathways -- The Abstract Neuron Model -- Emotional Models -- Generation of Music Data: J. Peterson and L. Dzuris -- Generation of Painting Data: J. Peterson, L. Dzuris and Q. Peterson -- Modeling Compositional Design -- Networks Of Excitable Neurons -- Training The Model -- Matrix Feed Forward Networks -- Chained Feed Forward Architectures -- Graph Models -- Address Based Graphs -- Building Brain Models -- Models of Cognitive Dysfunction -- Conclusions -- Background Reading. | |
520 | _aThis book shows how mathematics, computer science and science can be usefully and seamlessly intertwined. It begins with a general model of cognitive processes in a network of computational nodes, such as neurons, using a variety of tools from mathematics, computational science and neurobiology. It then moves on to solve the diffusion model from a low-level random walk point of view. It also demonstrates how this idea can be used in a new approach to solving the cable equation, in order to better understand the neural computation approximations. It introduces specialized data for emotional content, which allows a brain model to be built using MatLab tools, and also highlights a simple model of cognitive dysfunction. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer graphics. | |
650 | 0 | _aNeural networks (Computer science). | |
650 | 0 | _aPhysics. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aTheoretical, Mathematical and Computational Physics. |
650 | 2 | 4 | _aMathematical Models of Cognitive Processes and Neural Networks. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9789812878694 |
830 | 0 |
_aCognitive Science and Technology, _x2195-3988 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-981-287-871-7 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c53889 _d53889 |