| Hi-index | 0.00 |
The sigmoid function is very widely used as a neuron activation function in artificial neural networks, which makes its attributes a matter of some interest. This paper presents some general results on the derivatives of the sigmoid. These results relate the coefficients of various derivatives to standard number sequences from combinatorial theory, and thus provide a standard efficient way of calculating these derivatives. Such derivatives can be used in approximating the effect of input perturbations on the output of single neurons and could also be useful in statistical modeling applications.