In this paper we describe the hardware implementation of a spiking neuron model, which uses a spike time dependent plasticity (STDP) rule that allows synaptic changes by discrete time steps. For this purpose an integrate-and-fire neuron is used with recurrent local connections. The connectivity of this model has been set to 24 neighbours, so there is a high degree of parallelism. After obtaining good results with the hardware implementation of the model, we proceed to simplify this hardware description, trying to keep the same behaviour. Some experiments using dynamic grading patterns have been used in order to test the learning capabilities of the model. Finally, the serial implementation has been realized.