The spiking response of a biological neuron depends on the precise timing of afferent spikes. This temporal aspect of the neuronal code is essential in understanding information processing in neurobiology. In this model, raster plot analysis showed repeated activation of specific spiking motifs, which exhibit a precise temporal sequence of neural activations. Our first contribution is to develop a model for the efficient detection of temporal spiking motifs based on a layer of neurons with hetero-synaptic delays. Indeed, the variety of synaptic delays on the dendritic tree allows synchronizing synaptic inputs as they reach the basal dendritic tree. Second, we propose a bio-plausible unsupervised learning rule on both weights and delays through the derivation of a loss function which depends on the membrane potential of the spiking neuron and a sparseness regularization. We demonstrate on synthetic data that such a layer of spiking neurons is able to learn different repeating spatio-temporal motifs embedded in the spike train. Then, we test the robustness of the detection accuracy of the model by adding Poisson noise and compare it to a layer of Leaky-Integrate and Fire neurons trained with STDP. Results show a large improvement in performances when adding temporal delays for computations and a great increase in robustness to noise. We show that using synaptic delays for neuronal computations highly increases the representational capacities of a single neuron and its resilience to noise. .