Do Reservoir Computers Work Best at the Edge of Chaos?
Not on the map. Why?
Abstract (arXiv)
It has been demonstrated that cellular automata had the highest computational capacity at the edge of chaos, the parameter at which their behavior transitioned from ordered to chaotic. This same concept has been applied to reservoir computers; a number of researchers have stated that the highest computational capacity for a reservoir computer is at the edge of chaos, although others have suggested that this rule is not universally true. Because many reservoir computers do not show chaotic behavior but merely become unstable, it is felt that a more accurate term for this instability transition is the "edge of stability"Here I find two examples where the computational capacity of a reservoir computer decreases as the edge of stability is approached; in one case, because generalized synchronization breaks down, and in the other case because the reservoir computer is a poor match to the problem being solved. The edge of stability as an optimal operating point for a reservoir computer is not in general true, although it may be true in some cases.
Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 4; 2025: 4; 2026: 3.
17 papers.
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- 2023Generative learning for nonlinear dynamicscited by 22
- 2023Seeing double with a multifunctional reservoir computercited by 12
- 2022Catch-22s of reservoir computingcited by 11
- 2024Dynamical stability and chaos in artificial neural network trajectories along trainingcited by 6
- 2024How more data can hurt: Instability and regularization in next-generation reservoir computingcited by 5
- 2023Inferring Attracting Basins of Power System with Machine Learningcited by 4
- 2022Time Shifts to Reduce the Size of Reservoir Computerscited by 3
- 2024An introduction to reservoir computingcited by 2
- 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regimecited by 2
- 2026Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computingcited by 2
- 2024Neuromorphic on-chip reservoir computing with spiking neural network architecturescited by 1
- 2026Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaoscited by 1
5 more
- 2021Reservoir Computing with Magnetic Thin Filmscited by 0
- 2025Boosting Reservoir Computing with Brain-inspired Adaptive Dynamicscited by 0
- 2025Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of reservoir recurrent neural networkscited by 0
- 2025Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Propertycited by 0
- 2026Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecastingcited by 0
- 2026Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecasting20 Jul 2026
- 2026Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos22 Jun 2026
- 2026Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing16 Mar 2026
- 2025Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Property16 Dec 2025
- 2025Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of reservoir recurrent neural networks28 Apr 2025
- 2025Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics16 Apr 2025
- 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime25 Mar 2025
- 2024An introduction to reservoir computing12 Dec 2024
- 2024Neuromorphic on-chip reservoir computing with spiking neural network architectures30 Jul 2024
- 2024How more data can hurt: Instability and regularization in next-generation reservoir computing11 Jul 2024
- 2024Dynamical stability and chaos in artificial neural network trajectories along training8 Apr 2024
- 2023Generative learning for nonlinear dynamics7 Nov 2023
5 more
- 2023Inferring Attracting Basins of Power System with Machine Learning20 May 2023
- 2023Seeing double with a multifunctional reservoir computer9 May 2023
- 2022Catch-22s of reservoir computing18 Oct 2022
- 2022Time Shifts to Reduce the Size of Reservoir Computers3 May 2022
- 2021Reservoir Computing with Magnetic Thin Films29 Jan 2021
- 2023Generative learning for nonlinear dynamicscited by 22
- 2023Seeing double with a multifunctional reservoir computercited by 12
- 2022Catch-22s of reservoir computingcited by 11
- 2024Dynamical stability and chaos in artificial neural network trajectories along trainingcited by 6
- 2024How more data can hurt: Instability and regularization in next-generation reservoir computingcited by 5
- 2023Inferring Attracting Basins of Power System with Machine Learningcited by 4
- 2022Time Shifts to Reduce the Size of Reservoir Computerscited by 3
- 2024An introduction to reservoir computingcited by 2
- 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regimecited by 2
- 2026Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computingcited by 2
- 2024Neuromorphic on-chip reservoir computing with spiking neural network architecturescited by 1
- 2026Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaoscited by 1
12 most cited of 17 All 17 (JSON)
- 2026Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecasting20 Jul 2026
- 2026Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos22 Jun 2026
- 2026Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing16 Mar 2026
- 2025Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Property16 Dec 2025
- 2025Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of reservoir recurrent neural networks28 Apr 2025
- 2025Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics16 Apr 2025
- 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime25 Mar 2025
- 2024An introduction to reservoir computing12 Dec 2024
- 2024Neuromorphic on-chip reservoir computing with spiking neural network architectures30 Jul 2024
- 2024How more data can hurt: Instability and regularization in next-generation reservoir computing11 Jul 2024
- 2024Dynamical stability and chaos in artificial neural network trajectories along training8 Apr 2024
- 2023Generative learning for nonlinear dynamics7 Nov 2023
12 newest of 17 All 17 (JSON)