An analog VLSI pulsed neural network for image segmentation using adaptive connection weights is presented. The network marks segments in the image through synchronous firing patterns. The synchronization is achieved through adaption of connection weights. The adaption uses only local signals in a data-driven and self-organizing way. It is shown that for the proposed adaption rules a simple analog VLSI implementation is feasible due to the required local connections and the data-driven self-organizing approach. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Heittmann, A., Ramacher, U., Matolin, D., Schreiter, J., & Schüffny, R. (2002). An analog VLSI pulsed neural network for image segmentation using adaptive connection weights. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 1293–1298). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_209
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