A Hardware-friendly Neuromorphic Spiking Neural Network for Frequency Detection and Fine Texture Decoding

Michele Mastella, Elisabetta Chicca

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

2 Citations (Scopus)
137 Downloads (Pure)


Humans can distinguish fabrics by their textures, even when they are finer than the density of tactile sensors. Evidence suggests that this ability is produced by the nervous system using an active touch strategy. When the finger slides over a texture, the nervous system converts the texture’s spatial period into an equivalent spiking frequency. Many studies focused on modeling the biological encoding part that translates the spatial frequency into a temporal spiking frequency, but few explored the decoding part. In this work, we propose a novel approach based on a spiking neural network able to detect the frequency of an input signal. Inspired by biological evidence, our architecture detects the range in which the encoded frequency dwells and could therefore decode the texture’s spatial period. The network has been designed to be composed of existing neuromorphic spiking primitives. This property enables a straightforward implementation on integrated silicon circuits, allowing the texture decoding at the edge of the sensor.
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)


  • Power demand
  • Neuromorphics
  • Tactile sensors
  • Silicon
  • Decoding
  • Biological information theory
  • Biological neural networks

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