# Description The term **ReLU** stands for **Rectified Linear Unit**. It is one of the most widely used and significant types of neurons in modern neural networks. The structure of a **ReLU** [[Neuron]] is as follows: - [[Input or combination function]]: This is typically a weighted sum of inputs and values. - [[Activation function]]: The activation function corresponds to the rectifier function. The rectifier function is defined as: $ y = \text{max}(0, x) $ where: - $y = 0$ if $x \leq 0$ - $y = x$ if $x > 0$ This function allows the neuron to pass through positive values and output zero for any negative input. --- ## References - [[Deep learning - Anna Bosch Rué Jordi Casas Roma Toni Lozano Bagén]]