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¹School of Mathematical Sciences, Soochow University, Suzhou 215006, China
²School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
³Research Institute of Intelligent Complex Systems and Centre for Computational Systems Biology, Fudan University, Shanghai 200433, China
⁴Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
⁵State Key Laboratory of Medical Neurobiology, LCNBI, and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
⁶Potsdam Institute for Climate Impact Research (PIK), Potsdam 14473, Germany
⁷Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China

^*wlin@fudan.edu.cn
^†hfma@suda.edu.cn

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Articles Citing

Embedding theory of reservoir computing and reducing reservoir network using time delays

Reconstruction of interpretable causal network dynamics from time series data

Feedback-Free Photonic Reservoir Computing via Time-Concatenated Dimensional Expansion and Hybrid Pruning

Illuminating the black box of reservoir computing

Networked collective dynamics in animal ecology and cell biology

A general multi-order feature extractor for reservoir computing via simplicial complexes

Effect of time scale on chaotic time series prediction using echo state network

A caveat regarding the unfolding argument: implications of plasticity

Local active memristive oscillator enables controllable complex behaviours and frequency domain extraction

Recent research progress in reservoir computing

Embedding of low-dimensional sensory dynamics in recurrent networks: Implications for the geometry of neural representation

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Parameter-aware reservoir computing for learning parameterized nonlinear dynamical systems with delay and cumulative effects

Residual driven reservoir computing for tipping point identification and parameter drift decoding

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Delay-embedded reservoir computing with single memristor for scale-efficient temporal signal processing

Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction

Field‐Programable Dynamics in a Soft Magnetic Actuator Enabling True Random Number Generation and Reservoir Computing

Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing

Prediction of chaos in permanent magnet synchronous motor using weighted delayed feedback reservoir computing with multi-head attention

Smarter usage of measurement statistics can greatly improve continuous variable quantum reservoir computing

Interpreting reservoir computing through the equivalent visualization of its loss landscape

One-core neuron deep learning for time series prediction

Principled neuromorphic reservoir computing

Forecasting the forced van der Pol equation with frequent phase shifts using Reservoir Computing

Reconstructing the dynamics of coupled oscillators with cluster synchronization using parameter-aware reservoir computing

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