A single ReLU recurrent neural network with fixed weights and hidden dimension can uniformly approximate any continuous function on [-1,1] as its runtime increases. This is achieved via a new model, the Turing machine with neural units (TMNU), which balances algorithmic flexibility with bounded simulation by RNNs. The convergence rates match polynomial approximation rates, and minimax lower bounds confirm that runtime is an essential, unavoidable resource.