Next generation reservoir computing

Gauthier, Daniel J.; Bollt, Erik M.; Griffith, Aaron; Barbosa, Wendson A. S.

doi:10.1038/s41467-021-25801-2

Cited by 321 publications

(232 citation statements)

References 35 publications

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“…In both cases there is an improvement in the performance with the correct choice of the delayed input. It is has been demonstrated that the Lorenz x one step ahead prediction requires only the very recent history of the x-variable time series [13], and we find the optimal input delay of d = 1 to be consistent with this prior knowledge. For the Lorenz z cross-prediction task, on the other hand, there is a strong dependence on the history of the Lorenz x variable.…”

Section: Resultssupporting

confidence: 68%

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Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Jaurigue

Robertson

Wolters

et al. 2021

Entropy

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Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

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Section: Resultssupporting

confidence: 68%

“…In a recent paper [ 13 ], the authors aim to eliminate the issue of hyperparameter optimisation altogether by removing the reservoir. Their approach essentially takes the well-known nonlinear vector autoregression (NVAR) method, uses a less parsimonious approach to filling the feature vector, and adds Tikhonov regularisation.…”

Section: Introductionmentioning

confidence: 99%

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Jaurigue

Robertson

Wolters

et al. 2021

Entropy

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“…3a 126 with G 2 = 0) is reasonable. Moreover, this finding supports the general observation that only the very recent history of the x-variable time series [13], and we find the optimal parameters and was also demonstrated for a time-continuous system, indicating that 180 our approach is applicable to a wide range of reservoirs. for a reservoir to yield good performance on a range of tasks by only tuning the delayed 202 input parameters, remain to be determined.…”

supporting

confidence: 86%

“…In a recent paper [13], the authors aim to eliminate the issue of hyperparameter 31 optimisation altogether by removing the reservoir. Their approach essentially takes the 32 well known nonlinear vector autoregression (NVAR) method, uses a less parsimonious 33 approach to filling the feature vector and adds Tikhonov regularisation.…”

mentioning

confidence: 99%

“…Their approach essentially takes the 32 well known nonlinear vector autoregression (NVAR) method, uses a less parsimonious 33 approach to filling the feature vector and adds Tikhonov regularisation. However, 34 the method of [13] trades the optimisation of the reservoir hyperparameters for the 35 optimisation of the feature vector elements and it can not be asserted that the latter is 36 generally less costly. Furthermore, one of the main factors driving research into reservoir 37 have not been used to optimise the performance of reservoir computers.…”

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confidence: 99%

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Reservoir Computing with Delayed Input for Fast and Easy Optimization

Jaurigue¹,

Robertson²,

Wolters³

et al. 2021

Preprint

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Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

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Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications

Ju,

Kim

2024

Adv Funct Materials

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To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbOx/Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors.

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Next generation reservoir computing

Cited by 321 publications

References 35 publications

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir Computing with Delayed Input for Fast and Easy Optimization

Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications

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