Abstract
Since the standard error backpropagation algorithm for supervised learning was shown biologically implausible, alternative models of training that use only local activation variables have been proposed. In this paper we present a novel algorithm called UBAL, inspired by the GeneRec model. We shortly describe the model and show the performance of the algorithm for XOR and 4-2-4 problems.
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Acknowledgment
This work was supported by grants VEGA 1/0796/18 and KEGA 017UK-4/2016.
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Malinovská, K., Malinovský, Ľ., Farkaš, I. (2018). Towards More Biologically Plausible Error-Driven Learning for Artificial Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_23
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DOI: https://doi.org/10.1007/978-3-030-01424-7_23
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