Artificial Bio-inspired Tactile Receptive Fields for Edge Orientation Classification

Ali Dabbous*, Michele Mastella, Aishwarya Natarajan, Elisabetta Chicca, Maurizio Valle, Chiara Bartolozzi

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review


Robots and users of hand prosthesis could easily manipulate objects if endowed with the sense of touch. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. An important cue for objects discrimination is the orientation of edges, that is used both in artificial vision and touch as pre-processing stage. We present a spiking neural network, inspired on the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations (from 0o to 180o), by implementing a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike based learning, and that the number and spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination.
Originele taal-2English
Titel2021 IEEE International Symposium on Circuits and Systems (ISCAS)
Aantal pagina's5
ISBN van geprinte versie978-1-7281-9202-4
StatusPublished - 28-mei-2021
Evenement2021 IEEE International Symposium on Circuits and Systems (ISCAS) - Daegu, Korea (South)
Duur: 22-mei-202128-mei-2021


Conference2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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