http://www.bibsonomy.org/burst/user/tmalsburg/dynamicfieldtheoryBibSonomy BuRST Feed for /user/tmalsburg/dynamicfieldtheory2008-07-21T01:40:44+02:00http://www.bibsonomy.org/bibtex/2174c324de2a08c53d3948b6558392c7b/tmalsburgtmalsburg2008-05-05T10:06:03+02:00classic dynamicfieldtheory model lateralinteraction <span style="color:#555555;">Shun-Ichi <a href="http://www.bibsonomy.org/author/Amari">Amari</a> </span><em>Biological Cybernetics</em><em>27(2):77--87</em>(<em>1977</em>)Mon May 05 10:06:03 CEST 2008Biological Cybernetics277--87{Dynamics of pattern formation in lateral-inhibition type neural fields}271977classic dynamicfieldtheory model lateralinteraction The dynamics of pattern formation is studied for lateral-inhibition type homogeneous neural fields with general connections. Neural fields consisting of single layer are first treated, and it is proved that there are five types of pattern dynamics. The type of the dynamics of a field depends not only on the mutual connections within the field but on the level of homogeneous stimulus given to the field. An example of the dynamics is as follows: A fixed size of localized excitation, once evoked by stimulation, can be retained in the field persistently even after the stimulation vanishes. It moves until it finds the position of the maximum of the input stimulus. Fields consisting of an excitatory and an inhibitory layer are next analyzed. In addition to stationary localized excitation, fields have such pattern dynamics as production of oscillatory waves, travelling waves, active and dual active transients, etc.http://www.bibsonomy.org/bibtex/26317c0368626b3d3d55017a49a3de98f/tmalsburgtmalsburg2008-05-02T23:00:56+02:00dynamicfieldtheory selforganization model classic <span style="color:#555555;">Shun-Ichi <a href="http://www.bibsonomy.org/author/Amari">Amari</a> </span><em>Bulletin of Mathematical Biology</em><em>42(3):339--364</em>(<em>1980</em>)Fri May 02 23:00:56 CEST 2008Bulletin of Mathematical Biology3339--364{Topographic organization of nerve fields}421980dynamicfieldtheory selforganization model classic The vertebrate nervous system has topographic interconnections in many parts, known for example as retinotopy, somatotopy, etc. It is plausible that modifiable synapses play an important role in forming and refining these connections together with the sensory experiences. To elucidate the mechanism of topographic organization, we propose a simple model consisting of two nerve fields connected by modifiable excitatory synapses. The model also includes modifiable inhibitory synapses. The behavior of the model is described by a set of simultaneous non-linear integro-differential equations. By analyzing the equations, we obtain the equilibrium solution of topographic connections. It is also proved that a part of the presynaptic field which is frequently stimulated comes to be mapped on a large area of the postsynaptic field so that it has a good resolution.http://www.bibsonomy.org/bibtex/2abd889321fe501b8181be130419db171/tmalsburgtmalsburg2008-05-01T15:23:41+02:00motorcontrol dynamicfieldtheory vision changedetection workingmemory <span style="color:#555555;">Jeffrey S. <a href="http://www.bibsonomy.org/author/Johnson">Johnson</a> and John P. <a href="http://www.bibsonomy.org/author/Spencer">Spencer</a> and Gregor <a href="http://www.bibsonomy.org/author/Schoner">Schoner</a> </span><em>New Ideas in Psychology</em>(<em>2007</em>)Thu May 01 15:23:41 CEST 2008New Ideas in PsychologyMoving to higher ground: The dynamic field theory and the dynamics of visual cognition2007motorcontrol dynamicfieldtheory vision changedetection workingmemory In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the ‘binding problem’ in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior. http://www.bibsonomy.org/bibtex/23320d27f4cf30654a0bc87054b14399f/tmalsburgtmalsburg2008-05-01T13:45:42+02:00interference model dynamicalsystems changedetection dynamicfieldtheory workingmemory <span style="color:#555555;">J S <a href="http://www.bibsonomy.org/author/Johnson">Johnson</a> and J P <a href="http://www.bibsonomy.org/author/Spencer">Spencer</a> and G <a href="http://www.bibsonomy.org/author/Schöner">Sch&#246;ner</a> </span><em>Proceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006), </em><em>page399-404. </em><em>Vancouver, Canada, </em>(<em>2006</em>)Thu May 01 13:45:42 CEST 2008Vancouver, CanadaProceedings of the 28th Annual Conference of the Cognitive Science Society (CogSci 2006)399-404A Dynamic Neural Field Theory of Multi-Item Visual Working Memory and Change Detection2006interference model dynamicalsystems changedetection dynamicfieldtheory workingmemory Many visually-guided behaviors rely critically on the ability to maintain visual information in working memory. However, to date, there are few formal models of visual working memory (VWM) that directly interface with the growing empirical literature on this basic cognitive system. In particular, no current theories address both the maintenance of multiple items in VWM and the process of change detection within a neurally-plausible framework. In the present study, we describe such an approach, along with initial data from a change detection task that confirm a novel prediction of our model.http://www.bibsonomy.org/bibtex/24b05c8ddd8bafaa889c36aa9eb2929f9/tmalsburgtmalsburg2008-04-27T12:41:57+02:00dynamicfieldtheory tutorial robotics motorcontrol <span style="color:#555555;">W. <a href="http://www.bibsonomy.org/author/Erlhagen">Erlhagen</a> and E. <a href="http://www.bibsonomy.org/author/Bicho">Bicho</a> </span><em>Journal of Neural Engineering</em>(<em>2006</em>)Sun Apr 27 12:41:57 CEST 2008Journal of Neural Engineering36-54{The dynamic neural field approach to cognitive robotics}32006dynamicfieldtheory tutorial robotics motorcontrol Abstract This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner’s action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping–placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work. http://www.bibsonomy.org/bibtex/2a689d7c09cdd567a736ff43dd5bdbe34/tmalsburgtmalsburg2008-04-26T16:55:34+02:00eyemovements motorcontrol model inhibition dynamicfieldtheory <span style="color:#555555;">C. <a href="http://www.bibsonomy.org/author/Wilimzig">Wilimzig</a> and S. <a href="http://www.bibsonomy.org/author/Schneider">Schneider</a> and G. <a href="http://www.bibsonomy.org/author/Schoner">Schoner</a> </span><em>Neural Netw</em><em>19(8):1059--74</em>(<em>2006</em>)Sat Apr 26 16:55:34 CEST 2008Neural Netw81059--74{The time course of saccadic decision making: dynamic field theory.}192006eyemovements motorcontrol model inhibition dynamicfieldtheory Making a saccadic eye movement involves two decisions, the decision to initiate the saccade and the selection of the visual target of the saccade. Here we provide a theoretical account for the time-courses of these two processes, whose instabilities are the basis of decision making. We show how the cross-over from spatial averaging for fast saccades to selection for slow saccades arises from the balance between excitatory and inhibitory processes. Initiating a saccade involves overcoming fixation, as can be observed in the countermanding paradigm, which we model accounting both for the temporal evolution of the suppression probability and its dependence on fixation activity. The interaction between the two forms of decision making is demonstrated by predicting how the cross-over from averaging to selection depends on the fixation stimulus in gap-step-overlap paradigms. We discuss how the activation dynamics of our model may be mapped onto neuronal structures including the motor map and the fixation cells in superior colliculus.