更改

:<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>

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.

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。

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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==References==

== References==

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{{Reflist|40em}}

== External links==

==External links==

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*Telluride Neuromorphic Engineering Workshop

* Telluride Neuromorphic Engineering Workshop

*CapoCaccia Cognitive Neuromorphic Engineering Workshop

*CapoCaccia Cognitive Neuromorphic Engineering Workshop

* Institute of Neuromorphic Engineering

*Institute of Neuromorphic Engineering

* INE news site.

*INE news site.

* Frontiers in Neuromorphic Engineering Journal

*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

* 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>=