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Here, i and j respectively represent any neuron combination in the input layer and output layer. <math>t_i</math> and <math>t_j</math> respectively represent the state combinations of neurons in the input and output layers. These states are observed after intervening i as the maximum entropy distribution, under the condition that the neural network mechanism remains unchanged. In other words, intervening input neuron i to follow a uniform distribution causes a change in the output neuron. Then this value measures the mutual information between the two.

Here, <math>i</math> and <math>j</math> respectively represent any neuron combination in the input layer and output layer. <math>t_i</math> and <math>t_j</math> respectively represent the state combinations of neurons in the input and output layers. These states are observed after intervening i as the maximum entropy distribution, under the condition that the neural network mechanism remains unchanged. In other words, intervening input neuron <math>i</math> to follow a uniform distribution causes a change in the output neuron. Then this value measures the mutual information between the two.

Here, it should be distinguished from the definition of EI. Here, each neuron in the input layer is do-intervened separately, and then the mutual information calculated by each two neurons is accumulated as the definition of sensitivity. Degeneracy is obtained by the difference between EI and sensitivity and is defined as:

Here, it should be distinguished from the definition of EI. Here, each neuron in the input layer is do-intervened separately, and then the mutual information calculated by each two neurons is accumulated as the definition of sensitivity. Degeneracy is obtained by the difference between EI and sensitivity and is defined as: