A differentiable function is one that has a well-defined derivative at every point in its domain, meaning it can be locally approximated by a linear map. Neural network components must be differentiable, or approximated by a differentiable surrogate, for gradient-based optimisation to compute usable error signals. Automatic differentiation and backpropagation both depend on the composed network being a differentiable function of its parameters.