[Re] A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity
A replication of reward-modulated motor learning and automaticity in a reservoir-computing model.
External publication. Published by its original venue; not published in our journal.
How do learning curves and post-training variability depend on reservoir initialization under a fixed reward schedule?
Use evaluator-owned task targets, reward computations and seed schedules, and recompute trajectory errors from saved outputs.
What you could produce
- Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.
Before you use it
- Exact article-specific dependencies, data and build requirements remain uninspected.
Limits to keep in view
- No research code was executed; no independent scientific verification has been performed.
- The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
- Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.
Source and permission context
Sankar, Remya; Thou, Nicolas; Rougier, Nicolas P.; Leblois, Arthur. [Re] A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity. ReScience C 7(1), #11; 10.5281/zenodo.5718075.
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manuscript · CC-BY-4.0
The exact Zenodo record cited by the journal declares cc-by-4.0; the journal separately identifies published manuscripts as CC BY. This records manuscript rights, not separate code/data licenses or archive contents.
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