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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2512.14675 |
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| _version_ | 1866917149193273344 |
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| author | Chipera, Rae Du, Jenny Tsapara, Irene |
| author_facet | Chipera, Rae Du, Jenny Tsapara, Irene |
| contents | Contemporary reservoir computing relies heavily on smooth, globally Lipschitz continuous activation functions, limiting applications in defense, disaster response, and pharmaceutical modeling where robust operation under extreme conditions is critical. We systematically investigate non-smooth activation functions, including chaotic, stochastic, and fractal variants, in echo state networks. Through comprehensive parameter sweeps across 36,610 reservoir configurations, we demonstrate that several non-smooth functions not only maintain the Echo State Property (ESP) but outperform traditional smooth activations in convergence speed and spectral radius tolerance. Notably, the Cantor function (continuous everywhere and flat almost everywhere) maintains ESP-consistent behavior up to spectral radii of rho ~ 10, an order of magnitude beyond typical bounds for smooth functions, while achieving 2.6x faster convergence than tanh and ReLU. We introduce a theoretical framework for quantized activation functions, defining a Degenerate Echo State Property (d-ESP) that captures stability for discrete-output functions and proving that d-ESP implies traditional ESP. We identify a critical crowding ratio Q=N/k (reservoir size / quantization levels) that predicts failure thresholds for discrete activations. Our analysis reveals that preprocessing topology, rather than continuity per se, determines stability: monotone, compressive preprocessing maintains ESP across scales, while dispersive or discontinuous preprocessing triggers sharp failures. While our findings challenge assumptions about activation function design in reservoir computing, the mechanism underlying the exceptional performance of certain fractal functions remains unexplained, suggesting fundamental gaps in our understanding of how geometric properties of activation functions influence reservoir dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14675 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Lipschitz Continuity and Monotonicity: Fractal and Chaotic Activation Functions in Echo State Networks Chipera, Rae Du, Jenny Tsapara, Irene Machine Learning Contemporary reservoir computing relies heavily on smooth, globally Lipschitz continuous activation functions, limiting applications in defense, disaster response, and pharmaceutical modeling where robust operation under extreme conditions is critical. We systematically investigate non-smooth activation functions, including chaotic, stochastic, and fractal variants, in echo state networks. Through comprehensive parameter sweeps across 36,610 reservoir configurations, we demonstrate that several non-smooth functions not only maintain the Echo State Property (ESP) but outperform traditional smooth activations in convergence speed and spectral radius tolerance. Notably, the Cantor function (continuous everywhere and flat almost everywhere) maintains ESP-consistent behavior up to spectral radii of rho ~ 10, an order of magnitude beyond typical bounds for smooth functions, while achieving 2.6x faster convergence than tanh and ReLU. We introduce a theoretical framework for quantized activation functions, defining a Degenerate Echo State Property (d-ESP) that captures stability for discrete-output functions and proving that d-ESP implies traditional ESP. We identify a critical crowding ratio Q=N/k (reservoir size / quantization levels) that predicts failure thresholds for discrete activations. Our analysis reveals that preprocessing topology, rather than continuity per se, determines stability: monotone, compressive preprocessing maintains ESP across scales, while dispersive or discontinuous preprocessing triggers sharp failures. While our findings challenge assumptions about activation function design in reservoir computing, the mechanism underlying the exceptional performance of certain fractal functions remains unexplained, suggesting fundamental gaps in our understanding of how geometric properties of activation functions influence reservoir dynamics. |
| title | Beyond Lipschitz Continuity and Monotonicity: Fractal and Chaotic Activation Functions in Echo State Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.14675 |