Modelling perception with artificial neural networks

✍ Scribed by Ruxton, Graeme D.;Tosh, Colin

• Publisher: Cambridge University Press
• Year: 2010
• Tongue: English
• Leaves: 409
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Part I. General themes 1 2 Neural networks for perceptual processing: from simulation tools to theories Sensory ecology and perceptual allocation: new prospects for neural networks / Kevin Gurney / Steven M. Phelps. --
Part II. 3 4 5 6 7 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 / Alexander Borst / Robert L. White III and Lawrence H. Snyder / Francesco Mannella, Marco Mirolli and Gianluca Baldassarre / Raffae.e Calabretta. --
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 / 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 8 Part III. 9 10 11 12 13. 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 / 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 Part IV. 14 15 16 17 18 19.

✦ Subjects

Computersimulation;Neural networks (Computer science);Neuronales Netz;Perception--Computer simulation;Wahrnehmung;Perception -- Computer simulation