# Learn to Synchronize, Synchronize to Learn

@article{Verzelli2021LearnTS, title={Learn to Synchronize, Synchronize to Learn}, author={Pietro Verzelli and Cesare Alippi and Lorenzo Francesco Livi}, journal={Chaos}, year={2021}, volume={31 8}, pages={ 083119 } }

In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks, we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by…

## 6 Citations

Learning strange attractors with reservoir systems

- MathematicsArXiv
- 2021

This paper shows that the celebrated Embedding Theorem of Takens is a particular case of a much more general statement according to which, randomly generated linear state-space representations of…

Criticality in reservoir computer of coupled phase oscillators

- Computer SciencePhysical Review E
- 2022

An artificial neural network of coupled phase oscillators is designed and, by the technique of reservoir computing in machine learning, train it for predicting chaos and it is found that when the machine is properly trained, oscillators in the reservoir are synchronized into clusters whose sizes follow a power-law distribution.

Euler State Networks

- Computer ScienceArXiv
- 2022

Experiments on synthetic tasks indicate the marked superiority of the proposed approach, compared to standard RC models, in tasks requiring longterm memorization skills, and results on real-world time series classification benchmarks point out that EuSN is capable of matching (or even surpassing) the level of accuracy of trainable Recurrent Neural Networks.

Reservoir time series analysis: Using the response of complex dynamical systems as a universal indicator of change.

- Computer ScienceChaos
- 2022

This work presents the idea of reservoir time series analysis (RTSA), a method by which the state space representation generated by a reservoir computing model can be used for time seriesAnalysis and shows significant, generalized accuracy across the proposed RTSA features that surpasses the benchmark methods.

Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem.

- MathematicsPhysical review. E
- 2021

This paper shows that a large class of fading memory state-space systems driven by discrete-time observations of dynamical systems defined on compact manifolds always yields continuously…

Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems

- MathematicsPhysica D: Nonlinear Phenomena
- 2021

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