Neural Comp. Sign up for ETOCS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Hoshino, O.
Right arrow Articles by Kambara, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Hoshino, O.
Right arrow Articles by Kambara, T.
(Neural Computation. 2001;13:1781-1810.)
© 2001 The MIT Press


Letter

A Hierarchical Dynamical Map as a Basic Frame for Cortical Mapping and Its Application to Priming

Osamu Hoshino

Department of Human Welfare Engineering, Oita University, Otia 870-1192, Japan

Satoru Inoue

Department of Information Network Science, The University of Electrocommunications, Chofu, Tokyo 182-8585, Japan

Yoshiki Kashimori

Department of Applied Physics and Chemistry, The University of Electrocommunications, Chofu, Tokyo 182-8585, Japan

Takeshi Kambara

Department of Applied Physics and Chemistry, The University of Electrocommunications, Chofu, Tokyo 182-8585, Japan

A hierarchical dynamical map is proposed as the basic framework for sensory cortical mapping. To show how the hierarchical dynamical map works in cognitive processes, we applied it to a typical cognitive task known as priming, in which cognitive performance is facilitated as a consequence of prior experience. Prior to the priming task, the network memorizes a sensory scene containing multiple objects presented simultaneously using a hierarchical dynamical map. Each object is composed of different sensory features. The hierarchical dynamical map presented here is formed by random itinerancy among limit-cycle attractors into which these objects are encoded. Each limit-cycle attractor contains multiple point attractors into which elemental features belonging to the same object are encoded. When a feature stimulus is presented as a priming cue, the network state is changed from the itinerant state to a limit-cycle attractor relevant to the priming cue. After a short priming period, the network state reverts to the itinerant state. Under application of the test cue, consisting of some feature belonging to the object relevant to the priming cue and fragments of features belonging to others, the network state is changed to a limit-cycle attractor and finally to a point attractor relevant to the target feature. This process is considered as the identification of the target. The model consistently reproduces various observed results for priming processes such as the difference in identification time between cross-modality and within-modality priming tasks, the effect of interval between priming cue and test cue on identification time, the effect of priming duration on the time, and the effect of repetition of the same priming task on neural activity.




This article has been cited by other articles:


Home page
Neural Comput.Home page
O. Hoshino
Cognitive Enhancement Mediated Through Postsynaptic Actions of Norepinephrine on Ongoing Cortical Activity
Neural Comput., August 1, 2005; 17(8): 1739 - 1775.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
O. Hoshino
Neuronal Bases of Perceptual Learning Revealed by a Synaptic Balance Scheme
Neural Comput., March 1, 2004; 16(3): 563 - 594.
[Abstract] [Full Text] [PDF]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 2001 by The MIT Press.