000 03413nam a22005535i 4500
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
024 7 _a10.1007/978-981-287-871-7
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPeterson, James K.
_eauthor.
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.
300 _aXXXV, 570 p. 165 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 _aCognitive Science and Technology,
_x2195-3988
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