Rui Ponte Costa, Cláudia Soares, Ana Carolina Filipe
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Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compensation through forward prediction but fail to account for fast online corrections and rapid adaptation observed in biological systems. We propose a cerebellum-inspired control framework that combines multiplexed predictive representations with internal feedback. By jointly encoding kinematic variables and task-relevant error signals, the model enables accurate online correction despite delayed feedback. Furthermore, incorporating feedback within the cerebellar loop significantly accelerates adaptation, reducing learning time by an order of magnitude. Our results show that single-signal predictions are insufficient under delay, while multiplexing and feedback together provide a unified mechanism for online control and rapid learning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2609.29945")
get_code_for_paper("2609.29945")
have("2609.29945")
The run record, dated, one paper per request, free:
curl https://syntology.ai/api/ran/2609.29945.json
A badge for a README (the split and the date, never a ratio):
[](https://syntology.ai/paper/2609.29945)
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