שמור ב:
| Main Authors: | , , , , , , , , |
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| פורמט: | Preprint |
| יצא לאור: |
2026
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| נושאים: | |
| גישה מקוונת: | https://arxiv.org/abs/2604.14021 |
| תגים: |
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| _version_ | 1866908966894698496 |
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| author | Ferrari, Federica Davidhi, Flavia Maacaron, Bernard Motta, Alberto van Keeken, Luuk Donati, Elisa Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara |
| author_facet | Ferrari, Federica Davidhi, Flavia Maacaron, Bernard Motta, Alberto van Keeken, Luuk Donati, Elisa Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara |
| contents | Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-attractor network representing a robot joint angle through self-sustaining population activity. Local excitation and broad inhibition support a stable activity bump, while velocity-modulated asymmetries drive its translation and boundary conditions confine motion within mechanical limits. The network reproduces smooth trajectory tracking and remains stable near joint limits, showing reduced drift and improved accuracy compared to unbounded models. Such compact hardware-compatible implementation preserves multi-second stability demonstrating a near-linear relationship between bump velocity and synaptic modulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14021 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation Ferrari, Federica Davidhi, Flavia Maacaron, Bernard Motta, Alberto van Keeken, Luuk Donati, Elisa Indiveri, Giacomo De Luca, Chiara Bartolozzi, Chiara Robotics Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-attractor network representing a robot joint angle through self-sustaining population activity. Local excitation and broad inhibition support a stable activity bump, while velocity-modulated asymmetries drive its translation and boundary conditions confine motion within mechanical limits. The network reproduces smooth trajectory tracking and remains stable near joint limits, showing reduced drift and improved accuracy compared to unbounded models. Such compact hardware-compatible implementation preserves multi-second stability demonstrating a near-linear relationship between bump velocity and synaptic modulation. |
| title | Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.14021 |