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删除92字节 、 2022年4月28日 (四) 19:50
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:<math> \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S </math>
 
:<math> \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S </math>
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:\frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
      
as a function of the properties of the physical memristive network and the external sources. In the equation above, <math>\alpha</math> is the "forgetting" time scale constant, <math> \xi=r-1</math> and <math>r=\frac{R_\text{off}}{R_\text{on}}</math> is the ratio of ''off'' and ''on'' values of the limit resistances of the memristors, <math> \vec S </math> is the vector of the sources of the circuit and <math>\Omega</math> is a projector on the fundamental loops of the circuit. The constant <math>\beta</math> has the dimension of a voltage and is associated to the properties of the [[memristor]]; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector <math>W=\operatorname{diag}(\vec W)</math> and <math>\vec W</math> respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.
 
as a function of the properties of the physical memristive network and the external sources. In the equation above, <math>\alpha</math> is the "forgetting" time scale constant, <math> \xi=r-1</math> and <math>r=\frac{R_\text{off}}{R_\text{on}}</math> is the ratio of ''off'' and ''on'' values of the limit resistances of the memristors, <math> \vec S </math> is the vector of the sources of the circuit and <math>\Omega</math> is a projector on the fundamental loops of the circuit. The constant <math>\beta</math> has the dimension of a voltage and is associated to the properties of the [[memristor]]; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector <math>W=\operatorname{diag}(\vec W)</math> and <math>\vec W</math> respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.
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Caravelli-Traversa-Di Ventra方程是描述物理记忆网络和外部源性质的函数。在上述方程中,<math>\alpha</math>是“遗忘”时间尺度常数,<math>\xi=r-1</math>,<math>r =\frac{R\text_{off}}{R_\text{on}}</math>是记忆电阻器off状态和on状态极限电阻值之比,<math>\vec S</math>是电路源的矢量,<math>\Omega</math>是电路基本环路的投影。常数<math>\beta</math>具有电压的量纲,与记忆电阻器的特性有关;其物理原型是导体中的电荷迁移率。对角矩阵和向量 <math>W=\operatorname{diag}(\vec W)</math>和<nowiki><math>\vec W<math></nowiki>'''<font color="#32CD32">是记忆电阻器的内阻</font>''',值在0到1之间。因此,这个等式需要在'''<font color="32CD32">内存值</font>'''上添加额外约束以保证可靠性。
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Caravelli-Traversa-Di Ventra方程是描述物理记忆网络和外部源性质的函数。在上述方程中,<math>\alpha</math>是“遗忘”时间尺度常数,<math>\xi=r-1</math>,<math>r =\frac{R\text_{off}}{R_\text{on}}</math>是记忆电阻器off状态和on状态极限电阻值之比,<math>\vec S</math>是电路源的矢量,<math>\Omega</math>是电路基本环路的投影。常数<math>\beta</math>具有电压的量纲,与记忆电阻器的特性有关;其物理原型是导体中的电荷迁移率。对角矩阵和向量 <math>W=\operatorname{diag}(\vec W)</math>和<nowiki><math>\vec W</math></nowiki>'''<font color="#32CD32">是记忆电阻器的内阻</font>''',值在0到1之间。因此,这个等式需要在'''<font color="32CD32">内存值</font>'''上添加额外约束以保证可靠性。
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{{Portal bar|Electronics}}
 
{{Portal bar|Electronics}}
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==References==
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== References==
 
{{Reflist|40em}}
 
{{Reflist|40em}}
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== External links==
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==External links==
 
<!--======================== {{No more links}} ============================
 
<!--======================== {{No more links}} ============================
 
     | PLEASE BE CAUTIOUS IN ADDING MORE LINKS TO THIS ARTICLE. Wikipedia  |
 
     | PLEASE BE CAUTIOUS IN ADDING MORE LINKS TO THIS ARTICLE. Wikipedia  |
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*Telluride Neuromorphic Engineering Workshop
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* Telluride Neuromorphic Engineering Workshop
 
*CapoCaccia Cognitive Neuromorphic Engineering Workshop
 
*CapoCaccia Cognitive Neuromorphic Engineering Workshop
* Institute of Neuromorphic Engineering
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*Institute of Neuromorphic Engineering
* INE news site.
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*INE news site.
* Frontiers in Neuromorphic Engineering Journal
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*Frontiers in Neuromorphic Engineering Journal
 
*Computation and Neural Systems department at the California Institute of Technology.
 
*Computation and Neural Systems department at the California Institute of Technology.
 
*Human Brain Project official site
 
*Human Brain Project official site
*Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN
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* Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN
    
=<nowiki>外部链接</nowiki>=  
 
=<nowiki>外部链接</nowiki>=  
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