Differentiable in this context, means that it can be learned by gradient descent, which uses derivatives to adjust a neural network model's parameters to minimise some error measure. As for how that applies to a hash function, I think the lookup gives some sort of weighting for each possible output, for each input, with the largest weightings corresponding to a "match". But tbh I'm not certain on that last part...