What activation function addresses dying neuron problems by introducing a negative slope below 0?

The activation function that effectively addresses the dying neuron problem by introducing a negative slope for input values below zero is known as Leaky ReLU.

In traditional ReLU (Rectified Linear Unit), neurons can become inactive for any inputs less than or equal to zero, leading to the phenomenon known as "dying ReLU." This happens when the gradient is zero for these negative inputs, causing the neurons to stop learning because their weights are never updated (i.e., they become effectively "dead").

Leaky ReLU mitigates this issue by allowing a small, non-zero gradient when the input is negative. Specifically, it allows a small fraction of the negative inputs to pass through, which keeps the neurons alive and enables learning even in cases where the inputs are negative. This small slope effectively ensures that, during training, the weights associated with such neurons can still be adjusted, allowing them to contribute to the model's performance rather than becoming dormant.

The other options, while they have their own advantages and specific use cases, do not primarily address the dying neuron problem through a negative slope like Leaky ReLU does. For instance, ELU (Exponential Linear Unit) has a negative output for negative inputs but does not simply introduce a linear negative slope