Abstract
We will consider the dynamics of attractor neural networks with sign-constrained weights. In the presense of sign-constraints on weights three types of attractor can affect the dynamics: retrieval attractors, spurious attractors and uniform attractors. The uniform attracting states can dominate the dynamics if there is a substantial weight-sign bias. We will show that it is possible to define dynamic thresholds for a variety of learning rules which can eliminate uniform attracting states for any value of the weight-sign bias.
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References
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© 1991 Springer-Verlag Berlin Heidelberg
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Campbell, C. (1991). Dynamic thresholds and attractor neural networks. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035894
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DOI: https://doi.org/10.1007/BFb0035894
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