Reproducing the dynamics of biological neural systems using mixed signal analog/digital neuromorphic circuits makes these systems ideal platforms to implement low-power bio-inspired devices for a wide range of application domains. Despite these principled assets, neuromorphic system design has to cope with the limited resources presently available on hardware. Here, different spiking networks were designed, tested in simulation, and implemented on the neuromorphic processor DYNAP-SE, to obtain silicon neurons that are tuned to visual stimuli oriented at specific angles and with specific spatial frequencies, provided by the event camera DVS. Recurrent clustered inhibition was successfully tested on spiking neural networks, both in simulation and on the DYNAP-SE board, to obtain neurons with highly structured Gabor-like receptive fields (RFs); these neurons are characterized by tuning curves that are sharper or at least comparable to the ones obtained using equivalent feed-forward schem es, but require a significantly lower number of synapses. The resulting harmonic signal description provided by the proposed neuromorphic circuit could be potentially used for a complete characterization of the 2D local structure of the visual signal in terms of phase relationships from all the available oriented channels.