Abstract
Reservoir Computing (RC) is a consolidated framework for designing fastly trainable recurrent neural systems, where the dynamical component is fixed and initialized to implement a fading memory over the input signal. In this paper, we study the behavior of a recently introduced class of alternative RC approaches in which the fixed dynamical component implements a stable but non-dissipative system, so that the driving temporal signal can be propagated through multiple time steps effectively. We analyze the behavior of two classes of non-dissipative RC in terms of dynamical stability and show the resulting advantages in time-series classification tasks in comparison to conventional RC.
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Notes
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A matrix \(\textbf{M}\) is antisymmetric if \(\textbf{M}^T = -\textbf{M}\).
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Acknowledgments
This work has been supported by EU-EIC EMERGE (Grant No. 101070918), and by NEURONE, a project funded by the Italian Ministry of University and Research (PRIN 20229JRTZA).
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Gallicchio, C., Ceni, A. (2024). Non-dissipative Reservoir Computing Approaches for Time-Series Classification. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15025. Springer, Cham. https://doi.org/10.1007/978-3-031-72359-9_8
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