Cover of: Dynamic Neural Field Theory for Motion Perception | Martin A. Giese

Dynamic Neural Field Theory for Motion Perception

  • 257 Pages
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Springer US , Boston, MA
Psychology, clinical, Neurosciences, Computer vision, Ph
About the Edition

Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception. This framework uses dynamic neural fields as a key mathematical concept. The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural processes. Also, similar principles form a basis for the design of computer vision systems as well as the design of artificially behaving systems. The book discusses in detail the application of this theoretical approach to motion perception and will be of great interest to researchers in vision science, psychophysics, and biological visual systems.

Statementby Martin A. Giese
SeriesThe Springer International Series in Engineering and Computer Science -- 469, International series in engineering and computer science -- 469.
Classifications
LC ClassificationsQC174.7-175.36
The Physical Object
Format[electronic resource] /
Pagination1 online resource (xix, 257 pages).
ID Numbers
Open LibraryOL27033475M
ISBN 101461375533, 1461555817
ISBN 139781461375531, 9781461555810
OCLC/WorldCa851741970

Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception. This framework uses dynamic neural fields as a key mathematical concept.

The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural processes. Dynamic Neural Field Theory for Motion Perception by Martin A. Giese,available at Book Depository with free delivery worldwide.3/5(1).

Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception. The author demonstrates how neural fields can be applied for the analysis of perceptual phenomena and its underlying neural.

Download Dynamic Neural Field Theory for Motion Perception FB2

ISBN: OCLC Number: Description: 1 online resource (xix, pages) Contents: 1 Introduction --I Basic Concepts Visual perception of motion Basic principles of the dynamic approach Dynamic neural fields --II Model for Motion Perception Dynamic neural field model for motion perception Necessity of the concepts: Model for the motion.

Dynamic Thinking: A Primer on Dynamic Field Theory introduces the reader to a new approach to understanding cognitive and neural dynamics using the concepts of Dynamic Field Theory (DFT). Dynamic Neural Fields are formalizations of how neural populations represent the continuous dimensions of perceptual features, movements, and cognitive decisions.

Dynamic field theory provides an explanation for how the brain gives rise to behavior via the coordinated activity of populations of neurons. These neural populations, depicted in the dynamic field simulator below, make local decisions about behaviorally relevant events in the world.

Cite this chapter as: Giese M.A. () Dynamic neural fields. In: Dynamic Neural Field Theory for Motion Perception. The Springer International Series in Engineering and Computer Science, vol from book Artificial Neural Networks and An Account Based on Dynamic Field Theory. and pro- vide a theoretical framework for continued refinement of a theory for object-motion perception.

Dynamic Neural Field Theory for Motion Perception, Martin A. Giese,Science, pages. Dynamic Neural Field Theory for Motion Perception provides a new theoretical framework that permits a systematic analysis of the dynamic properties of motion perception.

This. 1. Introduction. Neural fields (NFs) have been introduced as mathematical descriptions of cortical neural tissues, where information processing takes place in the form of excitation patterns.They permit a systematic treatment of dynamical processes not only in distributed neural representations, but also in the context of the dynamical system approach to perception and behavior.

Details Dynamic Neural Field Theory for Motion Perception FB2

Mind as Motion is the first comprehensive presentation of the dynamical approach to cognition. It contains a representative sampling of original, current research on topics such as perception, motor control, speech and language, decision making, and development. Included are chapters by pioneers of the approach, as well as others applying the tools of dynamics to a wide range of new problems.

LORIA - Université de Lorraine, Nancy, France. LORIA - Université de Lorraine, Nancy, France. View Profile. Cesar Torres-Huitzil. PDF | OnMichael Berger and others published The Counter-Change Model of Motion Perception: An Account Based on Dynamic Field Theory |.

36 Foundations of Dynamic Field Theory So this chapter is quite ambitious. It presents the core ideas of DFT that permeate the entire book. It reviews the associated conceptual commit-ments while also trying to be pedagogical and clear. If the going gets rough, go to the end of the chapter.

There we will make the ideas concrete and practical. Dynamic Neural Fields and Peaks as Units of Representation. The architecture of the Dynamic Neural Field or DNF is based on the finding that in the central nervous systems of vertebrates metric information is commonly represented in the form of population codes (Erickson, ; Georgopoulos, ; deCharms & Zador, ).

This means that. University of Iowa; Iowa City: Developing a magic number: The dynamic field theory reveals why visual working memory capacity estimates differ across tasks and development. Simmering VR, Spencer JP, Schutte AR.

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales. Brain Research. A Neural Approach to Cognition Based on Dynamic Field Theory Jonas Lins and Gregor Schoner¨ 1 Introduction Much theoretical work has been dedicated to studying neural field equations at an abstract, mathematical level, focusing on the dynamic properties of the solutions (this book provides review of many of the latest efforts in this direction).

Dynamic Thinking: A Primer on Dynamic Field Theory introduces the reader to a new approach to understanding cognitive and neural dynamics using the concepts of Dynamic Field Theory (DFT). Dynamic Neural Fields are formalizations of how neural populations represent the continuous dimensions of perceptual features, movements, and cognitive decisions.

This chapter introduces the core concepts of neural dynamics on which dynamic field theory (DFT) is based. Behavior is generated by the central nervous system (CNS).

From this observation, the argument is made that the inner state of the CNS must be described by continuous variables that evolve continuously over time. The concept of activation is introduced to characterize the inner state of.

Discover Book Depository's huge selection of Martin A Giese books online. Free delivery worldwide on over 20 million titles. Part II presents a functional magnetic resonance imaging (fMRI) study investigating the neural processing of expression and identity information in dynamic faces.

Previous studies proposed a distributed neural system for face perception which distinguishes between invariant (e.g., identity) and changeable (e.g., expression) aspects of faces. A neural field is a continuous version of a neural network model accounting for dynamical pattern forming from populational firing activities in neural tissues.

These patterns include standing bumps, moving bumps, traveling waves, target waves, breathers, and spiral waves, many of them observed in various brain areas.

Dynamic Field Theory (DFT) is an established framework for neuro-modeling or neuro-inspired computing, well suited for challenging perception and motion related tasks.

However, their computational requirements, distributed storage and bandwidth needs make them difficult to design for real-world environments. In this paper, the digital hardware implementation of an event-based dynamic neural.

Dynamic Neural Field Theory for Motion Perception by Martin A. Giese and Publisher Springer. Save up to 80% by choosing the eTextbook option for ISBN:The print version of this textbook is ISBN: () Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration.

Journal of Computational Neuroscience() Timing over Tuning: Overcoming the Shortcomings of a Line Attractor during a Working Memory Task. Introduction.

Face perception provides a model for investigating fundamental issues of neural coding.

Description Dynamic Neural Field Theory for Motion Perception FB2

For example, faces in the natural environment are usually dynamic, and facial movements convey critical social signals including gaze direction, speech-related movements, and expressions of emotion and pain.

This chapter lays the conceptual and mathematical foundations of dynamic field theory (DFT). It first discusses continua of possible percepts and of possible motor actions and proposes activation fields defined over relevant feature dimensions as the universal format of the neural representations on which perceptual, motor, and cognitive processes are based.

The neural dynamics introduced in. Dynamic Field Theory (DFT) is an established framework for modeling embodied cognition. In DFT, elementary cognitive functions such as memory formation, formation of grounded representations, attentional processes, decision making, adaptation, and learning emerge from neuronal dynamics.

The basic computational element of this framework is a Dynamic Neural Field (DNF). A Dynamic Neural Field Model of Word Learning: /ch Word learning is a complex phenomenon because it is tied to many different behaviors that are linked to multiple perceptual and cognitive systems.

Further. Read the online version of the book» Order the book» First-edition Errata» About the book The book covers classical topics, including the Hodgkin-Huxley equations and Hopfield model, as well as modern developments in the field such as Generalized Linear Models and decision theory.

For the movement field, the preprocessing consists of a neural dynamic implementation of the counter‐change model of motion perception (Berger, Faubel, Norman, Hock, & Schöner, ).

Both perception fields always have stable peaks of activation when there are colored or moving objects in .process-oriented theory that enables the modeling of concrete acts of embodied cognition.

We believe that such a theory must be based on neuronal principles, that will make it compatible with the constraints imposed on in-formation processing in the Central Nervous System.

Dynamic Field Theory (DFT) grew out of this research agenda.Giese, M.A. Dynamic Neural Field Theory of Motion Perception, Kluwer Academic Publishers, Dordrecht, Netherlands, Giese, M.A., and T.

Poggio. Synthesis and Recognition of Biological Motion Patterns Based on Linear Superposition of Prototypical Motion Sequences.