Modelling perception with artificial neural networks /:
"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 mod...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Cambridge University Press,
2010.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource (x, 397 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781139042048 1139042041 1139044672 9781139044677 9780511779145 0511779143 |
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| 245 | 0 | 0 | |a Modelling perception with artificial neural networks / |c [edited by] Colin R. Tosh, Graeme D. Ruxton. |
| 246 | 3 | |a Modeling perception with artificial neural networks | |
| 260 | |a New York : |b Cambridge University Press, |c 2010. | ||
| 300 | |a 1 online resource (x, 397 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Part I. General themes: |g 1. |t Neural networks for perceptual processing: from simulation tools to theories / |r Kevin Gurney; |g 2. |t Sensory ecology and perceptual allocation: new prospects for neural networks / |r Steven M. Phelps -- |g Part II. |t The use of artificial neural networks to elucidate the nature of perceptual processes in animals: |g 3. |t Correlation versus gradient type motion detectors: the pros and cons / |r Alexander Borst; |g 4. |t Spatial constancy and the brain: insights from neural networks / |r Robert L. White III and Lawrence H. Snyder; |g 5. |t The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / |r Francesco Mannella, Marco Mirolli and Gianluca Baldassarre; |g 6. |t Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / |r Raffae.e Calabretta; |g 7. |t Effects of network structure on associative memory / |r Hiraku Oshima and Tokashi Odagaki; |g 8. |t Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / |r L. Douw [and others] -- |g Part III. |t Artificial neural networks as models of perceptual processing in ecology and evolutionary biology: |g 9. |t Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / |r Karin S. Pfennig and Michael J. Ryan; |g 10. |t Applying artificial neural networks to the study of prey coloration / |r Sami Merilaita; |g 11. |t Artificial neural networks in models of specialization, guild evolution and sympatric speciation / |r Noél M.A. Holmgren, Niclas. Norrstrom and Wayne M. Getz; |g 12. |t Probabilistic design principles for robust multimodal communication networks / |r David C. Krakauer, Jessica Flack and Nihat Ay; |g 13. |t Movement-based signalling and the physical world: modelling the changing perceptual task for receivers / |r Richard A. Peters -- |g Part IV. |t Methodological issues in the use of simple feedforward networks: |g 14. |t How training and testing histories affect generalization: a test of simple neural networks / |r Stefano Ghirlanda and Magnus Enquist; |g 15. |t The need for stochastic replication of ecological neural networks / |r Colin R. Tosh and Graeme D. Ruxton; |g 16. |t Methodological issues in modelling ecological learning with neural networks / |r Daniel W. Franks and Graeme D. Ruxton; |g 17. |t Neural network evolution and artificial life research / |r Dara Curran and Colin O'Riordan; |g 18. |t Current velocity shapes the functional connectivity of benthiscapes to stream insect movement / |r Julian D. Olden; |g 19. |t A model biological neural network: the cephalopod vestibular system / |r Roddy 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"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Perception |x Computer simulation. | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Biological models. |0 http://id.loc.gov/authorities/subjects/sh85014180 | |
| 650 | 1 | 2 | |a Perception |x physiology |
| 650 | 2 | 2 | |a Models, Biological |
| 650 | 2 | 2 | |a Neural Networks, Computer |
| 650 | 6 | |a Perception |x Simulation par ordinateur. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Modèles biologiques. | |
| 650 | 7 | |a MEDICAL |x Neuroscience. |2 bisacsh | |
| 650 | 7 | |a PSYCHOLOGY |x Neuropsychology. |2 bisacsh | |
| 650 | 7 | |a Biological models |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Perception |x Computer simulation |2 fast | |
| 700 | 1 | |a Tosh, Colin. | |
| 700 | 1 | |a Ruxton, Graeme D. | |
| 758 | |i has work: |a Modelling perception with artificial neural networks (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGCwgjqwwt9MMwFGrhmTpP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Modelling perception with artificial neural networks. |d New York : Cambridge University Press, 2010 |z 9780521763950 |w (DLC) 2010010418 |w (OCoLC)569508969 |
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| adam_text | |
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| author2 | Tosh, Colin Ruxton, Graeme D. |
| author2_role | |
| author2_variant | c t ct g d r gd gdr |
| author_additional | Kevin Gurney; Steven M. Phelps -- Alexander Borst; Robert L. White III and Lawrence H. Snyder; Francesco Mannella, Marco Mirolli and Gianluca Baldassarre; Raffae.e Calabretta; Hiraku Oshima and Tokashi Odagaki; L. Douw [and others] -- Karin S. Pfennig and Michael J. Ryan; Sami Merilaita; Noél M.A. Holmgren, Niclas. Norrstrom and Wayne M. Getz; David C. Krakauer, Jessica Flack and Nihat Ay; Richard A. Peters -- Stefano Ghirlanda and Magnus Enquist; Colin R. Tosh and Graeme D. Ruxton; Daniel W. Franks and Graeme D. Ruxton; Dara Curran and Colin O'Riordan; Julian D. Olden; Roddy Williamson and Abdul Chrachri. |
| author_facet | Tosh, Colin Ruxton, Graeme D. |
| author_sort | Tosh, Colin |
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| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QP441 |
| callnumber-raw | QP441 .M63 2010eb |
| callnumber-search | QP441 .M63 2010eb |
| callnumber-sort | QP 3441 M63 42010EB |
| callnumber-subject | QP - Physiology |
| collection | ZDB-4-EBA |
| contents | Neural networks for perceptual processing: from simulation tools to theories / Sensory ecology and perceptual allocation: new prospects for neural networks / The use of artificial neural networks to elucidate the nature of perceptual processes in animals: Correlation versus gradient type motion detectors: the pros and cons / Spatial constancy and the brain: insights from neural networks / The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / Effects of network structure on associative memory / Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / Artificial neural networks as models of perceptual processing in ecology and evolutionary biology: Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / Applying artificial neural networks to the study of prey coloration / Artificial neural networks in models of specialization, guild evolution and sympatric speciation / Probabilistic design principles for robust multimodal communication networks / Movement-based signalling and the physical world: modelling the changing perceptual task for receivers / Methodological issues in the use of simple feedforward networks: How training and testing histories affect generalization: a test of simple neural networks / The need for stochastic replication of ecological neural networks / Methodological issues in modelling ecological learning with neural networks / Neural network evolution and artificial life research / Current velocity shapes the functional connectivity of benthiscapes to stream insect movement / A model biological neural network: the cephalopod vestibular system / |
| ctrlnum | (OCoLC)710993028 |
| dewey-full | 612.8/2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 612 - Human physiology |
| dewey-raw | 612.8/2 |
| dewey-search | 612.8/2 |
| dewey-sort | 3612.8 12 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn710993028 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:45Z |
| institution | BVB |
| isbn | 9781139042048 1139042041 1139044672 9781139044677 9780511779145 0511779143 |
| language | English |
| oclc_num | 710993028 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (x, 397 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Cambridge University Press, |
| record_format | marc |
| spelling | Modelling perception with artificial neural networks / [edited by] Colin R. Tosh, Graeme D. Ruxton. Modeling perception with artificial neural networks New York : Cambridge University Press, 2010. 1 online resource (x, 397 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Part I. General themes: 1. Neural networks for perceptual processing: from simulation tools to theories / Kevin Gurney; 2. Sensory ecology and perceptual allocation: new prospects for neural networks / Steven M. Phelps -- Part II. The use of artificial neural networks to elucidate the nature of perceptual processes in animals: 3. Correlation versus gradient type motion detectors: the pros and cons / Alexander Borst; 4. Spatial constancy and the brain: insights from neural networks / Robert L. White III and Lawrence H. Snyder; 5. The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / Francesco Mannella, Marco Mirolli and Gianluca Baldassarre; 6. Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / Raffae.e Calabretta; 7. Effects of network structure on associative memory / Hiraku Oshima and Tokashi Odagaki; 8. Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / L. Douw [and others] -- Part III. Artificial neural networks as models of perceptual processing in ecology and evolutionary biology: 9. Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / Karin S. Pfennig and Michael J. Ryan; 10. Applying artificial neural networks to the study of prey coloration / Sami Merilaita; 11. Artificial neural networks in models of specialization, guild evolution and sympatric speciation / Noél M.A. Holmgren, Niclas. Norrstrom and Wayne M. Getz; 12. Probabilistic design principles for robust multimodal communication networks / David C. Krakauer, Jessica Flack and Nihat Ay; 13. Movement-based signalling and the physical world: modelling the changing perceptual task for receivers / Richard A. Peters -- Part IV. Methodological issues in the use of simple feedforward networks: 14. How training and testing histories affect generalization: a test of simple neural networks / Stefano Ghirlanda and Magnus Enquist; 15. The need for stochastic replication of ecological neural networks / Colin R. Tosh and Graeme D. Ruxton; 16. Methodological issues in modelling ecological learning with neural networks / Daniel W. Franks and Graeme D. Ruxton; 17. Neural network evolution and artificial life research / Dara Curran and Colin O'Riordan; 18. Current velocity shapes the functional connectivity of benthiscapes to stream insect movement / Julian D. Olden; 19. A model biological neural network: the cephalopod vestibular system / Roddy Williamson and Abdul Chrachri. "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"-- Provided by publisher Print version record. Perception Computer simulation. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Perception physiology Models, Biological Neural Networks, Computer Perception Simulation par ordinateur. Réseaux neuronaux (Informatique) Modèles biologiques. MEDICAL Neuroscience. bisacsh PSYCHOLOGY Neuropsychology. bisacsh Biological models fast Neural networks (Computer science) fast Perception Computer simulation fast Tosh, Colin. Ruxton, Graeme D. has work: Modelling perception with artificial neural networks (Text) https://id.oclc.org/worldcat/entity/E39PCGCwgjqwwt9MMwFGrhmTpP https://id.oclc.org/worldcat/ontology/hasWork Print version: Modelling perception with artificial neural networks. New York : Cambridge University Press, 2010 9780521763950 (DLC) 2010010418 (OCoLC)569508969 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=361548 Volltext |
| spellingShingle | Modelling perception with artificial neural networks / Neural networks for perceptual processing: from simulation tools to theories / Sensory ecology and perceptual allocation: new prospects for neural networks / The use of artificial neural networks to elucidate the nature of perceptual processes in animals: Correlation versus gradient type motion detectors: the pros and cons / Spatial constancy and the brain: insights from neural networks / The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / Effects of network structure on associative memory / Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / Artificial neural networks as models of perceptual processing in ecology and evolutionary biology: Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / Applying artificial neural networks to the study of prey coloration / Artificial neural networks in models of specialization, guild evolution and sympatric speciation / Probabilistic design principles for robust multimodal communication networks / Movement-based signalling and the physical world: modelling the changing perceptual task for receivers / Methodological issues in the use of simple feedforward networks: How training and testing histories affect generalization: a test of simple neural networks / The need for stochastic replication of ecological neural networks / Methodological issues in modelling ecological learning with neural networks / Neural network evolution and artificial life research / Current velocity shapes the functional connectivity of benthiscapes to stream insect movement / A model biological neural network: the cephalopod vestibular system / Perception Computer simulation. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Perception physiology Models, Biological Neural Networks, Computer Perception Simulation par ordinateur. Réseaux neuronaux (Informatique) Modèles biologiques. MEDICAL Neuroscience. bisacsh PSYCHOLOGY Neuropsychology. bisacsh Biological models fast Neural networks (Computer science) fast Perception Computer simulation fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85014180 |
| title | Modelling perception with artificial neural networks / |
| title_alt | Modeling perception with artificial neural networks Neural networks for perceptual processing: from simulation tools to theories / Sensory ecology and perceptual allocation: new prospects for neural networks / The use of artificial neural networks to elucidate the nature of perceptual processes in animals: Correlation versus gradient type motion detectors: the pros and cons / Spatial constancy and the brain: insights from neural networks / The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat / Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks / Effects of network structure on associative memory / Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition / Artificial neural networks as models of perceptual processing in ecology and evolutionary biology: Evolutionary diversification of mating behaviour: using artificial neural networks to study reproductive character displacement and speciation / Applying artificial neural networks to the study of prey coloration / Artificial neural networks in models of specialization, guild evolution and sympatric speciation / Probabilistic design principles for robust multimodal communication networks / Movement-based signalling and the physical world: modelling the changing perceptual task for receivers / Methodological issues in the use of simple feedforward networks: How training and testing histories affect generalization: a test of simple neural networks / The need for stochastic replication of ecological neural networks / Methodological issues in modelling ecological learning with neural networks / Neural network evolution and artificial life research / Current velocity shapes the functional connectivity of benthiscapes to stream insect movement / A model biological neural network: the cephalopod vestibular system / |
| title_auth | Modelling perception with artificial neural networks / |
| title_exact_search | Modelling perception with artificial neural networks / |
| title_full | Modelling perception with artificial neural networks / [edited by] Colin R. Tosh, Graeme D. Ruxton. |
| title_fullStr | Modelling perception with artificial neural networks / [edited by] Colin R. Tosh, Graeme D. Ruxton. |
| title_full_unstemmed | Modelling perception with artificial neural networks / [edited by] Colin R. Tosh, Graeme D. Ruxton. |
| title_short | Modelling perception with artificial neural networks / |
| title_sort | modelling perception with artificial neural networks |
| topic | Perception Computer simulation. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Biological models. http://id.loc.gov/authorities/subjects/sh85014180 Perception physiology Models, Biological Neural Networks, Computer Perception Simulation par ordinateur. Réseaux neuronaux (Informatique) Modèles biologiques. MEDICAL Neuroscience. bisacsh PSYCHOLOGY Neuropsychology. bisacsh Biological models fast Neural networks (Computer science) fast Perception Computer simulation fast |
| topic_facet | Perception Computer simulation. Neural networks (Computer science) Biological models. Perception physiology Models, Biological Neural Networks, Computer Perception Simulation par ordinateur. Réseaux neuronaux (Informatique) Modèles biologiques. MEDICAL Neuroscience. PSYCHOLOGY Neuropsychology. Biological models Perception Computer simulation |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=361548 |
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