neural-network模型中，在產生一個輸出值前units轉換它們的net-input數值為一個activation value並視為一個中介的步驟。很多架構省略這個中介的步驟並且直接到輸出值的產生。在這裡，先忽略這個activation value的複雜度，我們首要的工作是output value輸出值的產生。我們以一個微分方程式的形式來表示一個unit的output value。就好像是生物學中所提的同等事物一樣，units的輸出值是時間的動態函數。-neural-network model, generating an output value before the units converted to their net-input values for an activation value, and as an intermediate step. Many architecture omitted intermediate steps and go directly to the output value of production. Here, first, ignore the activation value of the complexity, our primary task is to output value output value generation. We are in the form of a differential equation to represent a unit of output value. Biology is like the same things mentioned in the same, units of output value is the dynamic function of time.