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Spike-timing-dependent plasticity (STDP) is a learning algorithm that is simple, biologically plausible, and powerful. Hence, one would expect STDP (likely in combination with other learning algorithms) to be a key component in cortical models of higher cognitive functions, such as language comprehension or production. Such models would need to involve multiple brain areas and bidirectional links between representations in those different areas. However, STDP is an asymmetrical learning algorithm (in contrast to classical Hebbian learning, which is symmetrical). This makes the acquisition of bilateral connections between two neurons almost impossible and bilateral connections between representations very challenging. Here, we propose a solution based on specific connectivity patterns. Then, using numerical simulations, we show that our approach allows STDP to create strong bidirectional links between representations. Finally, we compare our architecture to neuroanatomical data.