Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Reinforcement Learning Framework for Deep Brain Stimulation Study
Authors: Dmitrii Krylov, Remi Tachet des Combes, Romain Laroche, Michael Rosenblum, Dmitry V. Dylov
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We successfully suppress synchrony via RL for three pathological signaling regimes, characterize the framework s stability to noise, and further remove the unwanted oscillations by engaging multiple PPO agents. |
| Researcher Affiliation | Collaboration | 1Skolkovo Institute of Science and Technology, Bolshoy blvd. 30/1, Moscow, 121205, Russia 2Microsoft Research Lab, 550-2000 Mc Gill College Ave, Montr eal H3A 3H3, Canada 3University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam-Golm, Germany |
| Pseudocode | No | The paper includes a conceptual diagram (Figure 1) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/cviaai/RL-DBS/ |
| Open Datasets | No | The paper uses data generated by numerical solutions of ordinary differential equations (Eqs. 1 and 2) simulating neuronal models, rather than a publicly available dataset. |
| Dataset Splits | No | The paper does not mention explicit training, validation, or test dataset splits, as it uses simulated, dynamically generated data rather than fixed datasets. |
| Hardware Specification | No | The paper vaguely states that 'training was performed on CPU' but provides no specific hardware details like CPU model, memory, or GPU specifications. |
| Software Dependencies | No | The paper mentions using the 'Stable Baselines library' but does not specify its version number or other software dependencies with versions. |
| Experiment Setup | Yes | In our experiments, we used two-hidden layers MLPs with 64 neurons, trained using the Stable Baselines library [Hill et al., 2018], with the default parameters for PPO. ... γ is a discount factor that controls the tradeoff between long-term and immediate rewards (set to 0.99 in our experiments). ... For a given action A and a given observation Xstate at time t, we propose the following class of reward functions for synchrony suppression tasks: R t = X(t) Xstate t 2 β|A(t)| ... for β = 2 leads to convergence to the natural equilibrium point. |