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'''Reservoir computing''' is a framework for computation derived from [[recurrent neural network]] theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.<ref name=":4">{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085|issn=0893-6080|year=2019|doi-access=free}}</ref> After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.<ref name=":4" /> The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.<ref name=":4" /> The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.<ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pages=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|issn=2399-6528|doi-access=free}}</ref>

'''Reservoir computing''' is a framework for computation derived from [[recurrent neural network]] theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir.<ref name=":4">{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085|issn=0893-6080|year=2019|doi-access=free}}</ref> After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.<ref name=":4" /> The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed.<ref name=":4" /> The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.<ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pages=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|issn=2399-6528|doi-access=free}}</ref>

 

储备池计算是一从递归神经网络理论推导出的计算框架，它通过一个叫做储备池的固定的非线性系统内部的动力学，将输入信号映射到更高维的计算空间。当输入信号被送入储备池(储备池被视为一个“黑匣子”)后，一个简单的读出机制被训练来读取储备池中神经元的状态并将其映射到所需的输出。这个框架的第一个关键好处是，训练只在读出阶段进行，在读出阶段储备池动力学特性保持不变。第二个好处是这个储备池系统的计算能力，无论是在经典力学还是量子力学中，都可以用它来降低有效的计算成本。

 

油藏计算是一个由递归神经网络理论导出的计算框架，它通过一个固定的非线性系统，称为油藏的动力学，将输入信号映射到更高维的计算空间。当输入信号被送入储存器(被视为一个“黑匣子”)后，一个简单的读出机制被训练来读取储存器的状态并将其映射到所需的输出。这个框架的第一个关键好处是，训练只在读出阶段进行，因为储层动态是固定的。第二是自然可用系统的计算能力，无论是经典的还是量子力学的，都可以用来降低有效的计算成本。

== History ==

== History ==