Abstract
We introduce a model of working memory combining short-term and long-term components. For the long-term component, we used Conceptors in order to store constant temporal patterns. For the short-term component, we used the Gated-Reservoir model: a reservoir trained to hold a triggered information from an input stream and maintain it in a readout unit. We combined both components in order to obtain a model in which information can go from long-term memory to short-term memory and vice-versa.
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Jaeger, H.: Controlling recurrent neural networks by conceptors. arXiv preprint arXiv:1403.3369 (2014)
Jaeger, H.: Using conceptors to manage neural long-term memories for temporal patterns. J. Mach. Learn. Res. 18(13), 1–43 (2017)
Strock, A., Hinaut, X., Rougier, N.P.: A robust model of gated working memory. biorXiv (2019). https://doi.org/10.1101/589564
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Strock, A., Rougier, N., Hinaut, X. (2019). Using Conceptors to Transfer Between Long-Term and Short-Term Memory. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_2
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DOI: https://doi.org/10.1007/978-3-030-30493-5_2
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