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The liquid (i.e. reservoir) of a Chaotic Liquid State Machine (CLSM), or chaotic reservoir, is made from chaotic spiking neurons but which stabilize their activity by settling to a single hypothesis that describes the trained inputs of the machine. This is in contrast to general types of reservoirs that don’t stabilize. The liquid stabilization occurs via synaptic plasticity and chaos control that govern neural connections inside the liquid. CLSM showed promising results in learning sensitive time series data.

The liquid (i.e. reservoir) of a Chaotic Liquid State Machine (CLSM), or chaotic reservoir, is made from chaotic spiking neurons but which stabilize their activity by settling to a single hypothesis that describes the trained inputs of the machine. This is in contrast to general types of reservoirs that don’t stabilize. The liquid stabilization occurs via synaptic plasticity and chaos control that govern neural connections inside the liquid. CLSM showed promising results in learning sensitive time series data.

该液体(即。混沌液态机(CLSM)或混沌液态机(CLSM)中的混沌液态机(reservoir)是由混沌脉冲神经元构成，但它们通过建立一个描述机器训练输入的单一假设来稳定其活动。这与一般不稳定的储层形成了鲜明的对比。液体的稳定是通过突触可塑性和混沌控制来实现的，混沌控制控制着液体内部的神经连接。CLSM 在学习敏感时间序列数据方面取得了良好的效果。

一个混沌液体状态机(CLSM)中的液态(比如储备池)或者混沌储备池，是由混沌脉冲神经元构成，但它们通过确立一个描述机器的被训练的输入的单一假设来稳定其活动。这与通常不稳定类型的储备池形成了鲜明的对比。液态稳定化是通过突触可塑性以及管理着液态内部的神经连接的混沌控制来实现的。CLSM 在学习敏感时间序列数据方面取得了良好的效果。

==== Nonlinear transient computation ====

==== Nonlinear transient computation ====

This type of information processing is most relevant when time-dependent input signals depart from the mechanism’s internal dynamics. These departures cause transients or temporary altercations which are represented in the device’s output.

This type of information processing is most relevant when time-dependent input signals depart from the mechanism’s internal dynamics. These departures cause transients or temporary altercations which are represented in the device’s output.

= = = = = 非线性瞬态计算 = = = = = 这种类型的信息处理是最相关的时间依赖的输入信号离开机制的内部动态。这些偏离引起瞬态或暂时的变化，这些变化在设备的输出中得到了体现。

非线性瞬态计算  

 

==== Deep reservoir computing ====

==== Deep reservoir computing ====

The extension of the reservoir computing framework towards Deep Learning, with the introduction of Deep Reservoir Computing and of the Deep Echo State Network (DeepESN) model allows to develop efficiently trained models for hierarchical processing of temporal data, at the same time enabling the investigation on the inherent role of layered composition in recurrent neural networks.

The extension of the reservoir computing framework towards Deep Learning, with the introduction of Deep Reservoir Computing and of the Deep Echo State Network (DeepESN) model allows to develop efficiently trained models for hierarchical processing of temporal data, at the same time enabling the investigation on the inherent role of layered composition in recurrent neural networks.

* = = = = = = = = = = = = 深度油藏计算扩展到深度学习的油藏计算框架，引入了深度油藏计算和深度回波状态网络(DeepESN)模型，允许开发有效训练的时间数据层次化处理模型，同时允许研究层状组合在回归神经网络中的固有作用。

 

随着深度储备池计算和深度回波状态网络(DeepESN)模型的出现，储备池计算框架开始向深度学习扩展，发展了有效的可训练模型来对时间数据进行多层次处理，同时使层状组合在循环神经网络中的固有作用的研究得以进行。

== Quantum reservoir computing ==

== Quantum reservoir computing ==

Quantum reservoir computing may use the nonlinear nature of quantum mechanical interactions or processes to form the characteristic nonlinear reservoirs but may also be done with linear reservoirs when the injection of the input to the reservoir creates the nonlinearity. The marriage of machine learning and quantum devices is leading to the emergence of quantum neuromorphic computing as a new research area.

Quantum reservoir computing may use the nonlinear nature of quantum mechanical interactions or processes to form the characteristic nonlinear reservoirs but may also be done with linear reservoirs when the injection of the input to the reservoir creates the nonlinearity. The marriage of machine learning and quantum devices is leading to the emergence of quantum neuromorphic computing as a new research area.

 

=== Types ===

=== Types ===