On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map

2005 ◽  
pp. 676-685 ◽  
Author(s):  
Peter Tiňo ◽  
Igor Farkaš
Keyword(s):  
Receptive Fields ◽  
Self Organizing Map ◽  
Topographic Organization ◽  
Self Organizing

scholarly journals Dynamics and Topographic Organization of Recursive Self-Organizing Maps

2006 ◽  
Vol 18 (10) ◽  
pp. 2529-2567 ◽  
Author(s):  
Peter Tiňo ◽  
Igor Farkaš ◽  
Jort van Mourik
Keyword(s):  
Receptive Fields ◽  
Topographic Maps ◽  
Sequential Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Fixed Input ◽  
Feedback Connections ◽  
Superior Memory ◽  
Vectorial Data ◽  
Self Organizing

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizing map (SOM) for processing sequential data, recursive SOM(RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data.

Breaking Rotational Symmetry in a Self-Organizing Map Model for Orientation Map Development

1998 ◽  
Vol 10 (3) ◽  
pp. 717-730 ◽  
Author(s):  
M. Riesenhuber ◽  
H.-U. Bauer ◽  
D. Brockmann ◽  
T. Geisel
Keyword(s):  
Pattern Formation ◽  
Rotational Symmetry ◽  
Ocular Dominance ◽  
Receptive Fields ◽  
Self Organizing Map ◽  
Orientation Map ◽  
Domain Boundaries ◽  
Formation Behavior ◽  
Som Model ◽  
Self Organizing

We analyze the pattern formation behavior of a high-dimensional self-organizing map (SOM) model for the competitive projection of ON-center-type and OFF-center-type inputs to a common map layer. We mathematically show, and numerically confirm, that even isotropic stimuli can drive the development of oriented receptive fields and an orientation map in this model. This result provides an important missing link in the spectrum of pattern formation behaviors observed in SOM models. Extending the model by including further layers for binocular inputs, we also investigate the combined development of orientation and ocular dominance maps. A parameter region for combined patterns exists; corresponding maps show a preference for perpendicular intersection angles between iso-orientation lines and ocularity domain boundaries, consistent with experimental observations.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing
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