Impact of Plasticity-Based Reservoir Adaptation on Spectral Radius and Performance of ESNs
Weber F, Belanche-Muñoz L, Maier A (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer
Pages Range: 163-175
Conference Proceedings Title: Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions
Event location: Kaunas
ISBN: 9783032045515
DOI: 10.1007/978-3-032-04552-2_16
Abstract
Echo state networks (ESNs) are recurrent neural networks belonging to the reservoir computing framework. While ESNs are conceptually simple, their successful application can be challenging. For instance, there are no generally applicable methods for optimally setting important hyperparameters like the reservoir spectral radius. Therefore, the development of strategies for appropriately initializing ESNs is an active field of research. Plasticity-based pretraining is a bio-inspired reservoir optimization approach. We analyze if this approach is able to improve the results of a non-optimized ESN and if the pretraining effects can be explained by the influence on the spectral radius. In our experiments, we evaluate the effects of four synaptic plasticity rules (SP), namely anti-Oja’s, normalized anti-Hebbian, BCM, and dual-threshold BCM, and of intrinsic plasticity (IP) on the Mackey-Glass, NARMA, and Lorenz series. IP significantly improves the ESN’s performance across all three benchmarks whereas this is not the case for any of the considered SP rules. Overall, the influence of plasticity on the spectral radius is not sufficient for explaining the pretraining effects. The cases, in which plasticity significantly worsens the results, however, can be explained by the spectral radius having been moved to a disadvantageous value.
Authors with CRIS profile
Franziska Weber
Lehrstuhl für Informatik 8 (Theoretische Informatik)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Spain (ES)How to cite
APA:
Weber, F., Belanche-Muñoz, L., & Maier, A. (2025). Impact of Plasticity-Based Reservoir Adaptation on Spectral Radius and Performance of ESNs. In Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions (pp. 163-175). Kaunas, LT: Springer.
MLA:
Weber, Franziska, Lluís Belanche-Muñoz, and Andreas Maier. "Impact of Plasticity-Based Reservoir Adaptation on Spectral Radius and Performance of ESNs." Proceedings of the 34th International Conference on Artificial Neural Networks, Kaunas Springer, 2025. 163-175.
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