Real-time Closed Loop Neural Decoding on a Neuromorphic Chip
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Abstract (arXiv)
This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of approximately 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of approximately 85% of those obtained by hand-controlled joystick . These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Dec 2018.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2.
3 papers.
- 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfacescited by 3
- 2025Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMIcited by 0
- 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networkscited by 0
- 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks23 Oct 2025
- 2025Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI9 May 2025
- 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces26 Dec 2023