Create Efficient and Complex Reservoir Computing Architectures with ReservoirPy

Trouvain, Nathan; Rougier, Nicolas P.; Hinaut, Xavier

doi:10.1007/978-3-031-16770-6_8

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…They are physically implemented through diverse technologies [61,62]. Consequently, many software frameworks and libraries have been developed and published in the literature [63,64].…”

Section: Reservoir Computing Modelmentioning

confidence: 99%

Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing

Arbateni,

Benzaoui

2024

Applied Sciences

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Electrocardiography (ECG) is a simple and safe tool for detecting heart conditions. Despite the diaspora of existing heartbeat classifiers, improvements such as real-time heartbeat identification and patient-independent classification persist. Reservoir computing (RC) based heartbeat classifiers are an emerging computational efficiency solution that is potentially recommended for real-time concerns. However, multiclass patient-independent heartbeat classification using RC-based classifiers has not been considered and constitutes a challenge. This study investigates patient-independent heartbeat classification by leveraging traditional RC and next-generation reservoir computing (NG-RC) solely or in a cascade. Three RCs were investigated for classification tasks: a linear RC featuring linear internal nodes, a nonlinear RC with a nonlinear internal node, and an NG-RC. Each of these has been evaluated independently using either linear ridge regression or multilayer perceptron (MLP) as readout models. Only three classes were considered for classification: the N, V, and S categories. Techniques to deal with the imbalanced nature of the data, such as the synthetic minority oversampling technique (SMOTE) and oversampling by replacement, were used. The MIT-BIH dataset was used to evaluate classification performance. The area under the curve (AUC) criterion was used as an evaluation metric. The NG-RC-based model improves classification performance and mitigates the overfitting issue. It has improved classification performance by 4.18% and 2.31% for the intra-patient and inter-patient paradigms, respectively. By cascading RC and NG-RC, the identification performance of the three heartbeat categories is further enhanced. AUCs of 97.80% and 92.09% were reported for intra- and inter-patient scenarios, respectively. These results suggest promising opportunities to leverage RC technology for multiclass, patient-independent heartbeat recognition.

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“…They are physically implemented through diverse technologies [61,62]. Consequently, many software frameworks and libraries have been developed and published in the literature [63,64].…”

Section: Reservoir Computing Modelmentioning

confidence: 99%

Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing

Arbateni,

Benzaoui

2024

Applied Sciences

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“…It aims to provide a simple and flexible framework to work with ESNs and other models. Additionally, [315] present a Python library that facilitates the creation of RC architectures, from ESNs and FORCE learning, to complex networks such as DeepESNs and other advanced architectures with complex connectivity between multiple reservoirs with feedback loops.…”

Section: Easy-access Tools Coding Framework and Recipesmentioning

confidence: 99%

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Zhang

Vargas

2023

IEEE Access

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Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps lowdimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.

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Create Efficient and Complex Reservoir Computing Architectures with ReservoirPy

Cited by 2 publications

References 27 publications

Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing

Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

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