Abstract
Hebbian learning rule is well known as a memory storing scheme for associative memory models on neural networks. However, this rule doesn’t work well in storing correlated memory patterns. Recently, a new method has been proposed based on pseudo-orthogonalization by XOR masking of original memory patterns with random patterns in order to overcome this problem. In this paper, we propose an extended method for pseudo-orthogonalization of memory patterns utilizing complex-valued and quaternionic neural networks. We demonstrate that Hebbian learning rule successfully stabilizes the correlated memory patterns, and these networks can retrieve the stored patterns corresponding to the external stimuli.
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Minemoto, T., Isokawa, T., Nishimura, H., Matsui, N. (2014). Utilizing High-Dimensional Neural Networks for Pseudo-orthogonalization of Memory Patterns. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_66
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DOI: https://doi.org/10.1007/978-3-319-12637-1_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12636-4
Online ISBN: 978-3-319-12637-1
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