Artificial Bio-inspired Tactile Receptive Fields for Edge Orientation Classification

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
228 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2021 IEEE International Symposium on Circuits and Systems (ISCAS)
Number of pages5
ISBN (Print)978-1-7281-9202-4
Publication statusPublished - 28-May-2021
Event2021 IEEE International Symposium on Circuits and Systems (ISCAS) - Daegu, Korea (South)
Duration: 22-May-202128-May-2021


Conference2021 IEEE International Symposium on Circuits and Systems (ISCAS)


  • Neuromorphics
  • Biological system modeling
  • Neurons
  • Skin
  • Encoding
  • Sensors
  • Biological information theory

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