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Neural Network Models of Cognition by J.W. Donahoe,V.P. Dorsel Pdf
This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
Introduction to Neural and Cognitive Modeling by Daniel S. Levine Pdf
This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value. Features of the second edition include: * A new section on spatiotemporal pattern processing * Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks * A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex * Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems. For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/
Animal Learning and Cognition by Nestor A. Schmajuk Pdf
In this advanced text, the author, starting with the simple assumption that psychological associations are represented by the strength of synaptic connections, details several mechanistic descriptions of complex cognitive behaviors. Part I presents neural network theories of classical conditioning; Part II describes neural networks of operant conditioning, and animal communication; Part III discusses spatial and cognitive mapping, and finally, Part IV shows how neural network models permit one to simultaneously develop psychological theories and models of the brain. The book includes computer software that allows the computer simulation of classical conditioning and the effect of different brain lesions on many classical paradigms. All those people interested in neural networks, from psychologists, through neuroscientists to computer scientists working on artificial intelligence and robotics, will find this book an excellent advanced guide to the subject.
Introduction to Neural and Cognitive Modeling by Daniel S. Levine Pdf
This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value. Features of the second edition include: * A new section on spatiotemporal pattern processing * Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks * A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex * Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems. For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/
Connectionist Models of Cognition and Perception II by Howard Bowman,Christophe Labiouse Pdf
This book collects together refereed versions of papers presented at the Eighth Neural Computation and Psychology Workshop (NCPW 8). NCPW is a well-established workshop series that brings together researchers from different disciplines, such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology. The articles are centred on the theme of connectionist modelling of cognition and perceptionn. The proceedings have been selected for coverage in: • Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings) • Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings) • Index to Social Sciences & Humanities Proceedings® (ISSHP® / ISI Proceedings) • Index to Social Sciences & Humanities Proceedings (ISSHP CDROM version / ISI Proceedings) • CC Proceedings — Engineering & Physical Sciences • CC Proceedings — Biomedical, Biological & Agricultural Sciences Contents:An Extended Buffer Model for Active Maintenance and Selective Updating (E J Davelaar & M Usher)Applying Forward Models to Sequence Learning: A Connectionist Implementation (D Theofilou, A Destrebecqz & A Cleeremans)Modelling Asymmetric Infant Categorization with the Representational Acuity Hypothesis (G Westermann & D Mareschal)Solving the Visual Expertise Mystery (C A Joyce & G W Cottrell)Through Attention to Consciousness by CODAM (J G Taylor)Modeling Visual Search: Evolving the Selective Attention for Identification Model (SAIM) (D Heinke, G W Humphreys & C L Tweed)A Temporal Attractor Framework for the Development of Analogical Completion (R Leech, D Mareschal & R Cooper)On the Evolution of Irrational Behaviour (J A Bullinaria)Reading, Sublexical Units and Scrambled Words: Capturing the Human Data (R C Shillcock & P Monaghan)and other papers Readership: Graduate students, academics and researchers in neural networks, artificial intelligence and psychology. Keywords:Neural Networks;Connectionism;Psychology;Perception;Cognition
Neural Network Models of Conditioning and Action by Michael L. Commons,Stephen Grossberg,John Staddon Pdf
Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
Computational Models of Cognitive Processes by Julien Mayor,Pablo Gomez Pdf
Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes. Contents:Language:Modelling Language — Vision Interactions in the Hub and Spoke Framework (A C Smith, P Monaghan and F Huettig)Modelling Letter Perception: The Effect of Supervision and Top-Down Information on Simulated Reaction Times (M Klein, S Frank, S Madec and J Grainger)Encoding Words into a Potts Attractor Network (S Pirmoradian and A Treves)Unexpected Predictability in the Hawaiian Passive (Ō Parker Jones and J Mayor)Difference Between Spoken and Written Language Based on Zipf's Law Analysis (J S Kim, C Y Lee and B T Zhang)Reading Aloud is Quicker than Reading Silently: A Study in the Japanese Language Demonstrating the Enhancement of Cognitive Processing by Action (H-F Yanai, T Konno and A Enjyoji)Development:Testing a Dynamic Neural Field Model of Children's Category Labelling (K E Twomey and J S Horst)Theoretical and Computational Limitations in Simulating 3- to 4-Month-Old Infants' Categorization Processes (M Mermillod, N Vermeulen, G Kaminsky, E Gentaz and P Bonin)Reinforcement-Modulated Self-Organization in Infant Motor Speech Learning (A S Warlaumont)A Computational Model of the Headturn Preference Procedure: Design, Challenges, and Insights (C Bergmann, L Ten Bosch and L Boves)Right Otitis Media in Early Childhood and Language Development: An ERP Study (M F Alonso, P Uclés and P Saz)High-Level Cognition:The Influence of Implementation on “Hub” Models of Semantic Cognition (O Guest, R P Cooper and E J Davelaar)Hierarchical Structure in Prefrontal Cortex Improves Performance at Abstract Tasks (R Tukker, A C Van Rossum, S Frank and W F G Haselager)Interactive Activation Networks for Modelling Problem Solving (P Monaghan, T Ormerod and U N Sio)On Observational Learning of Hierarchies in Sequential Tasks: A Dynamic Neural Field Model (E Sousa, W Erlhagen and E Bicho)Knowing When to Quit on Unlearnable Problems: Another Step Towards Autonomous Learning (T R Shultz and E Doty)A Conflict/Control-Loop Hypothesis of Hemispheric Brain Reserve Capacity (N Rendell and E J Davelaar)Action and Emotion:Modeling the Actor-Critic Architecture by Combining Recent Work in Reservoir Computing and Temporal Difference Learning in Complex Environments (J J Rodny and D C Noelle)The Conceptualisation of Emotion Qualia: Semantic Clustering of Emotional Tweets (E Y Bann and J J Bryson)A Neuro-Computational Study of Laughter (M F Alonso, P Loste, J Navarro, R Del Moral, R Lahoz-Beltra and P C Marijuán) Readership: Students and researchers in biocybernetics, neuroscience, cognitive science, psychology and artificial intelligence and those interested in neural models of psychological phenomena. Keywords:Cognitive Science;Computational Modeling;Psychology;Neural NetworksKey Features:An invaluable resource for researchers interested in neural models of psychological phenomenaEnables readers to catch up with a fast moving discipline by reading contributions that are typically published as journal articles only a couple of years laterOffers an overview of current computational models of cognitive processes in a single bookChapters are written by world-leading experts in the field
Neural Network Models in Artificial Intelligence by Matthew Zeidenberg Pdf
The aim of this book is to provide a concise introduction to recent, representative work in the field of neural networks. Each topic provides an overview of work in one particular area and proceeds towards a review of current research and development in that area.
Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay
Author : Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay Publisher : Springer Science & Business Media Page : 200 pages File Size : 42,5 Mb Release : 2009 Category : Computers ISBN : 9783540732457
Neural-Symbolic Cognitive Reasoning by Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay Pdf
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Connectionist Models in Cognitive Psychology by George Houghton Pdf
Connectionist Models in Cognitive Psychology is a state-of-the-art review of neural network modelling in core areas of cognitive psychology including: memory and learning, language (written and spoken), cognitive development, cognitive control, attention and action. The chapters discuss neural network models in a clear and accessible style, with an emphasis on the relationship between the models and relevant experimental data drawn from experimental psychology, neuropsychology and cognitive neuroscience. These lucid high-level contributions will serve as introductory articles for postgraduates and researchers whilst being of great use to undergraduates with an interest in the area of connectionist modelling.
Fundamentals of Neural Network Modeling by Randolph W. Parks,Daniel S. Levine,Debra L. Long Pdf
Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Introduction to Neural and Cognitive Modeling by Daniel S. Levine Pdf
This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field’s history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions. The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.
