Name
Abstract | ||
|---|---|---|
The objective of our work is a better understanding of the learning and the geometric structure of cortical signal representation. Many models for the stimulus-induced self-organization of topographic cortical representations are restricted to the spatial encoding of stimuli. However, such approaches cannot explain certain neurobiological findings. Therefore, we present a generalized approach based on temporal signal relations and time-to-space trans formations. The approach allows a larger class of signal topologies to be learned. We stress the importance of temporal signal relations for the function and development of cortical topography, explain neurobiological findings, and predict time-organized representational structures in cortical areas representing signals with systematic temporal relations. (C) 2002 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0925-2312(02)00505-2 | NEUROCOMPUTING |
Keywords | Field | DocType |
topography,self-organizing map,spatiotemporal stimuli,wave dynamics | Pattern recognition,Topographic map,Self-organizing map,Network topology,Artificial intelligence,Stimulus (physiology),Mathematics,Machine learning,Encoding (memory) | Journal |
Volume | ISSN | Citations |
44 | 0925-2312 | 1 |
PageRank | References | Authors |
0.41 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J Wiemer | 1 | 19 | 3.15 |
| W von Seelen | 2 | 503 | 140.13 |