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clustered inhibition, proving its efficacy in the con-
text of discrete spiking neural networks. We also ver-
ified that the linearity assumption holds despite the
high non-linearity of spiking neurons. Additionally,
we showed how it is possible to combine such fea-
ture detectors to generate filters with arbitrary phase
values, effectively implementing a full harmonic rep-
resentation of the image signal. The harmonic sig-
nal description provided by the proposed neuromor-
phic circuit could be potentially used for a complete
characterization of the 2D local structure of the vi-
sual signal in terms of the phase relationships from all
the available oriented channels. This would pave the
way to the implementation of complex bio-inspired
networks for more demanding on-line visual tasks on
neuromorphic hardware.
ACKNOWLEDGEMENTS
This project has received funding from the European
Research Council under the Grant Agreement No.
724295 (NeuroAgents).
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