000			04948cam a22003374a 4500
001			16128460
003			OSt
005			20150408114136.0
008			100310s2010 nyua b 001 0 eng
010			_a 2010010418
020			_a9780521763950
035			_a(OCoLC)ocn569508969
040			_aMa-Un
042			_apcc
050	0	0	_aQP441 _b.M63 2010
082	0	0	_a612.82 _222 _bMOP
245	0	0	_aModelling perception with artificial neural networks / _c[edited by] Colin R. Tosh, Graeme D. Ruxton.
260			_aNew York : _bCambridge University Press, _c2010.
300			_ax, 397 p. : _bill. ; _c26 cm.
504			_aIncludes bibliographical references and index.
505	0	0	_gPart I. General themes: _g1. _tNeural networks for perceptual processing: from simulation tools to theories / _rKevin Gurney; _g2. _tSensory ecology and perceptual allocation: new prospects for neural networks / _rSteven M. Phelps -- _gPart II. _tThe use of artificial neural networks to elucidate the nature of perceptual processes in animals: _g3. _tCorrelation versus gradient type motion detectors: the pros and cons / _rAlexander Borst; _g4. _tSpatial constancy and the brain: insights from neural networks / _rRobert L. White III and Lawrence H. Snyder; _g5. _tThe interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / _rFrancesco Mannella, Marco Mirolli and Gianluca Baldassarre; _g6. _tEvolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / _rRaffae.e Calabretta; _g7. _tEffects of network structure on associative memory / _rHiraku Oshima and Tokashi Odagaki; _g8. _tNeural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / _rL. Douw ... [et al.] -- _gPart III. _tArtificial neural networks as models of perceptual processing in ecology and evolutionary biology: _g9. _tEvolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / _rKarin S. Pfennig and Michael J. Ryan; _g10. _tApplying artificial neural networks to the study of prey coloration / _rSami Merilaita; _g11. _tArtificial neural networks in models of specialization, guild evolution and sympatric speciation / _rNo�el M. A. Holmgren, Niclas. Norrstrom and Wayne M. Getz; _g12. _tProbabilistic design principles for robust multimodal communication networks / _rDavid C. Krakauer, Jessica Flack and Nihat Ay; _g13. _tMovement-based signalling and the physical world: modelling the changing perceptual task for receivers / _rRichard A. Peters -- _gPart IV. _tMethodological issues in the use of simple feedforward networks: _g14. _tHow training and testing histories affect generalization: a test of simple neural networks / _rStefano Ghirlanda and Magnus Enquist; _g15. _tThe need for stochastic replication of ecological neural networks / _rColin R. Tosh and Graeme D. Ruxton; _g16. _tMethodological issues in modelling ecological learning with neural networks / _rDaniel W. Franks and Graeme D. Ruxton; _g17. _tNeural network evolution and artificial life research / _rDara Curran and Colin O'Riordan; _g18. _tCurrent velocity shapes the functional connectivity of benthiscapes to stream insect movement / _rJulian D. Olden; _g19. _tA model biological neural network: the cephalopod vestibular system / _rRoddy Williamson and Abdul Chrachri.
520			_a"Studies of the evolution of animal signals and sensory behaviour have more recently shifted from considering 'extrinsic' (environmental) determinants to 'intrinsic' (physiological) ones. The drive behind this change has been the increasing availability of neural network models. With contributions from experts in the field, this book provides a complete survey of artificial neural networks. The book opens with two broad, introductory level reviews on the themes of the book: neural networks as tools to explore the nature of perceptual mechanisms, and neural networks as models of perception in ecology and evolutionary biology. Later chapters expand on these themes and address important methodological issues when applying artificial neural networks to study perception. The final chapter provides perspective by introducing a neural processing system in a real animal. The book provides the foundations for implementing artificial neural networks, for those new to the field, along with identifying potential research areas for specialists"--
650		0	_aPerception _xComputer simulation. _916312
650		0	_aNeural networks (Computer science)
700	1		_aTosh, Colin. _936421
700	1		_aRuxton, Graeme D. _936422
856	4	2	_3Cover image _uhttp://assets.cambridge.org/97805217/63950/cover/9780521763950.jpg
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBOOK
999			_c30307 _d264807