Which activation function is designed to promote self-normalizing properties in neural networks?

The activation function specifically designed to promote self-normalizing properties in neural networks is the Scaled Exponential Linear Unit (SELU). It achieves self-normalization by automatically adjusting the mean and variance of the activations during the training process. This characteristic helps to maintain a consistent scale of activations across different layers, which can improve the stability and performance of deep learning models.

SELU functions by scaling the output of neurons in a way that encourages the activations to approach a zero mean and unit variance, which aligns well with the theoretical properties of neural networks trained using techniques like the maxout activation function. This self-normalizing effect is particularly useful in deep networks, as it reduces issues related to internal covariate shift, helping to standardize inputs to each layer.

In contrast, while other activation functions like ReLU and GeLU have their advantages, they do not inherently promote self-normalization to the same extent that SELU does. Adam, being an optimizer rather than an activation function, does not fit in this context. Therefore, SELU is the correct choice due to its unique self-normalizing properties and beneficial impact on neural network training dynamics.