Editing Artificial neuron (section)

===Sigmoid===
{{Main|Sigmoid function}}
A fairly simple nonlinear function, the [[sigmoid function]] such as the logistic function also has an easily calculated derivative, which can be important when calculating the weight updates in the network. It thus makes the network more easily manipulable mathematically, and was attractive to early computer scientists who needed to minimize the computational load of their simulations. It was previously commonly seen in [[multilayer perceptron]]s. However, recent work has shown sigmoid neurons to be less effective than [[Rectifier (neural networks)|rectified linear]] neurons. The reason is that the gradients computed by the [[backpropagation]] algorithm tend to diminish towards zero as activations propagate through layers of sigmoidal neurons, making it difficult to optimize neural networks using multiple layers of sigmoidal neurons.<!--
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