Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent plasticity mechanisms, which are intrinsically unsupervised, can be exploited to make a neural network achieve any computational task remains unknown. Here, we present a self-contained procedure which, through appropriate spatio-temporal stimulation control of the neurons, is able to drive rate-based neural networks with arbitrary initial connectivity towards a desired functional state. We illustrate our approach on two different tasks: a non-linear association between multiple input stimulations and activity patterns (representing digit images), and the construction of a continuous attractor encoding a collective variable in a neural population. Our work thus provides a proof of principle for emerging paradigms of in vitro biological computation, e.g. based on brain organoids.