PrincetonUniversity/PsyNeuLink
Feb 2025 – Aug 2025
4 Months active
Languages Used
PythonRSTreStructuredTextrst
Technical Skills
Bug FixCode RefactoringDeep LearningDeep Learning FrameworksDocumentationMachine Learning
Younes Strittmatter
PROFILE
Younes Strittmatter
Younes Strittmatter contributed to the PrincetonUniversity/PsyNeuLink repository by developing and refining computational neuroscience models, including the Botvinick conflict monitoring and Cohen-Huston 1994 models. He focused on aligning model implementations with original research, improving numerical stability in SoftMax functions, and enhancing graph visualization APIs for programmatic workflows. Using Python and PyTorch, Younes refactored code for maintainability, introduced CLI options for simulation control, and updated documentation for clarity and reproducibility. He also strengthened testing and CI processes, ensuring reliable releases. His work demonstrated depth in cognitive modeling, numerical computing, and scientific software engineering, resulting in more robust and reproducible experiments.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
26Total
Bugs
1
Commits
26
Features
8
Lines of code
7,850
Activity Months4
Your Network
49 people
Work History
August 2025
7 Commits • 2 Features
Aug 1, 2025Summary for 2025-08 (PrincetonUniversity/PsyNeuLink): Delivered key features, fixed critical bugs, and strengthened testing/reproducibility. Key features delivered include: Cohen-Huston 1994 Model Enhancements; Botvinick Conflict Monitoring Model core enhancements; test data bug fix for Cohen-Huston. The Cohen-Huston 1994 model was refactored to align with the original paper, with revised parameters and mechanism definitions, updated projection connections, trial setup, and plotting, plus comprehensive documentation updates. The Botvinick model script was refactored to remove obsolete code, introduced CLI options for trial counts, and refined integration rate and bias; accompanied by updated docs. The Cohen-Huston test data output was updated to reflect changes and maintain test accuracy. Overall impact: improved simulation fidelity to the literature, more reliable and reproducible experiments, and reduced ongoing maintenance through cleaner code and better documentation. Technologies/skills demonstrated: Python refactoring, CLI/tooling enhancements, test data management, documentation practices, and disciplined version control.
7 Commits • 2 Features
Aug 1, 2025Summary for 2025-08 (PrincetonUniversity/PsyNeuLink): Delivered key features, fixed critical bugs, and strengthened testing/reproducibility. Key features delivered include: Cohen-Huston 1994 Model Enhancements; Botvinick Conflict Monitoring Model core enhancements; test data bug fix for Cohen-Huston. The Cohen-Huston 1994 model was refactored to align with the original paper, with revised parameters and mechanism definitions, updated projection connections, trial setup, and plotting, plus comprehensive documentation updates. The Botvinick model script was refactored to remove obsolete code, introduced CLI options for trial counts, and refined integration rate and bias; accompanied by updated docs. The Cohen-Huston test data output was updated to reflect changes and maintain test accuracy. Overall impact: improved simulation fidelity to the literature, more reliable and reproducible experiments, and reduced ongoing maintenance through cleaner code and better documentation. Technologies/skills demonstrated: Python refactoring, CLI/tooling enhancements, test data management, documentation practices, and disciplined version control.
August 2025
July 2025
5 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: Key features delivered include integrating the Botvinick conflict monitoring model into the PsyNeuLink library and substantial improvements to the EVC OCM example script. No explicit bug fixes were reported in this period; the focus was on feature delivery, refactoring for maintainability, and clearer modeling workflows. Overall impact: expanded cognitive control modeling capabilities within PsyNeuLink, with more robust, easier-to-extend example scripts that support reproducible experiments. Technologies demonstrated: Python, PsyNeuLink, Botvinick conflict monitoring model, Drift-Diffusion Model (DDM), ControlSignal objects, ProcessingMechanism, and visualization optimization.
July 2025
5 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary: Key features delivered include integrating the Botvinick conflict monitoring model into the PsyNeuLink library and substantial improvements to the EVC OCM example script. No explicit bug fixes were reported in this period; the focus was on feature delivery, refactoring for maintainability, and clearer modeling workflows. Overall impact: expanded cognitive control modeling capabilities within PsyNeuLink, with more robust, easier-to-extend example scripts that support reproducible experiments. Technologies demonstrated: Python, PsyNeuLink, Botvinick conflict monitoring model, Drift-Diffusion Model (DDM), ControlSignal objects, ProcessingMechanism, and visualization optimization.
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for PrincetonUniversity/PsyNeuLink focusing on documentation and CI improvements. No major bug fixes were recorded this month; the team concentrated on improving developer experience and release reliability.
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for PrincetonUniversity/PsyNeuLink focusing on documentation and CI improvements. No major bug fixes were recorded this month; the team concentrated on improving developer experience and release reliability.
June 2025
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) — PrincetonUniversity/PsyNeuLink: Hardened numerical stability for SoftMax and enabled programmatic graph access. Delivered robust masking across NumPy/PyTorch, expanded test coverage, updated documentation, and enhanced graph visualization API for automation.
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 (2025-02) — PrincetonUniversity/PsyNeuLink: Hardened numerical stability for SoftMax and enabled programmatic graph access. Delivered robust masking across NumPy/PyTorch, expanded test coverage, updated documentation, and enhanced graph visualization API for automation.
Activity
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Quality Metrics
Correctness84.6%
Maintainability84.6%
Architecture81.6%
Performance76.6%
AI Usage20.8%
Skills & Technologies
Programming Languages
PythonRSTreStructuredTextrst
Technical Skills
Bug FixCode CleanupCode DocumentationCode RefactoringCognitive ModelingComputational NeuroscienceControl SystemsDeep LearningDeep Learning FrameworksDocumentationMachine LearningModel ConfigurationModel DescriptionModel ImplementationModel Refactoring
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