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Peer-reviewedCC BYFrontiers in Computational Neuroscience

Orientation detection for self-calibrating neuromorphic systems

  • Alejandro Pequeño-Zurro1
  • Alejandro Pequeño-Zurro2
  • Chiara Bartolozzi3
  • Elisabetta Chicca1
  • Elisabetta Chicca2
  1. 1Bio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials, University of Groningen, Groningen, Netherlands
  2. 2Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, Groningen, Netherlands
  3. 3Event-Driven Perception for Robotics, Istituto Italiano di Tecnologia (IIT), Genova, Italy
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Abstract

Bioinspired sensory architectures help build adaptive touch systems with efficient hardware implementations. Orientation detection is a fundamental capability for manipulating objects in real-world scenarios, with diverse applications in robotic manipulation and manufacturing assessment processes. In this work, we built spiking neural networks that combine biologically inspired receptive fields with unsupervised, local, spike-driven learning rules and self-regulating network dynamics to achieve orientation detection. The system is fully compatible with current neuromorphic hardware that operates in online learning conditions. Receptive fields were constructed using predefined connectivity based on biological findings in the somatosensory system, and the system was evaluated on edge orientation discrimination tasks against standard approaches in artificial systems. The system produces sparse, compressed neural representations, supporting energy-efficient neuromorphic implementations. Results show that random and unstructured receptive fields, which spread their inputs across the skin, outperform the regular kernel receptive field on this spatial discrimination task. The advantage is largest at fine angular resolution and small stimulus offset. These findings demonstrate that receptive field design is a key determinant of performance and present a biologically inspired framework for building self-calibrating, low-power tactile perception systems.

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Cite

BibTeXRIS
BibTeX
@article{pequenozurro2026orientation,
  title = {{Orientation detection for self-calibrating neuromorphic systems}},
  author = {Alejandro Pequeño-Zurro and Alejandro Pequeño-Zurro and Chiara Bartolozzi and Elisabetta Chicca and Elisabetta Chicca},
  journal = {Frontiers in Computational Neuroscience},
  year = {2026},
  month = oct,
  doi = {10.3389/fncom.2026.1878557},
  url = {https://doi.org/10.3389/fncom.2026.1878557},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Orientation detection for self-calibrating neuromorphic systems
AU  - Alejandro Pequeño-Zurro
AU  - Alejandro Pequeño-Zurro
AU  - Chiara Bartolozzi
AU  - Elisabetta Chicca
AU  - Elisabetta Chicca
PY  - 2026
DA  - 2026/10/23
JO  - Frontiers in Computational Neuroscience
DO  - 10.3389/fncom.2026.1878557
UR  - https://doi.org/10.3389/fncom.2026.1878557
AB  - Bioinspired sensory architectures help build adaptive touch systems with efficient hardware implementations. Orientation detection is a fundamental capability for manipulating objects in real-world scenarios, with diverse applications in robotic manipulation and manufacturing assessment processes. In this work, we built spiking neural networks that combine biologically inspired receptive fields with unsupervised, local, spike-driven learning rules and self-regulating network dynamics to achieve orientation detection. The system is fully compatible with current neuromorphic hardware that operates in online learning conditions. Receptive fields were constructed using predefined connectivity based on biological findings in the somatosensory system, and the system was evaluated on edge orientation discrimination tasks against standard approaches in artificial systems. The system produces sparse, compressed neural representations, supporting energy-efficient neuromorphic implementations. Results show that random and unstructured receptive fields, which spread their inputs across the skin, outperform the regular kernel receptive field on this spatial discrimination task. The advantage is largest at fine angular resolution and small stimulus offset. These findings demonstrate that receptive field design is a key determinant of performance and present a biologically inspired framework for building self-calibrating, low-power tactile perception systems.
SN  - 1662-5188
ER  -