Limitations of the recall capabilities in delay based reservoir computing systems
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Abstract (arXiv)
We analyze the memory capacity of a delay based reservoir computer with a Hopf normal form as nonlinearity and numerically compute the linear as well as the higher order recall capabilities. A possible physical realisation could be a laser with external cavity, for which the information is fed via electrical injection. A task independent quantification of the computational capability of the reservoir system is done via a complete orthonormal set of basis functions. Our results suggest that even for constant readout dimension the total memory capacity is dependent on the ratio between the information input period, also called the clock cycle, and the time delay in the system. Optimal performance is found for a time delay about 1.6 times the clock cycle
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021Master memory function for delay-based reservoir computers with single-variable dynamicscited by 3
- 2021Infinite-dimensional Folded-in-time Deep Neural Networkscited by 0
- 2026Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstrationcited by 0
- 2026Information Processing Capacity of Stationary Physical Systems: Theory, Data-efficient Estimation Methods, and Photonic Demonstration18 May 2026
- 2021Master memory function for delay-based reservoir computers with single-variable dynamics28 Aug 2021
- 2021Infinite-dimensional Folded-in-time Deep Neural Networks8 Jan 2021