README.md

Ref: Sankar, R., Leblois, A. and Rougier, N.P., 2022. Dual pathway architecture underlying vocal learning in songbirds. Pre-print.

This folder contains a the models presented in the abovementioned paper in Python using Jupyter notebooks.

Author: Remya Sankar

Contents

2 directories:-

• SyrinxModel: Contains a re-implementation of a syrinx model by Amador, et. al (https://doi.org/10.1038/nature11967). The contour generated using this avian syrinx model, is used to test the dual pathway model.
• Model: Contains the scripts for the dual pathway architecture and the corresponding benchmark model using a single pathway framework.

Figures of the paper:-

• To generate Fig2, use SyrinxModel/syrinx-amador.ipynb and Model/DualPathwayModel/DualPathwayModel.ipynb.
• To generate Fig3, use Model/DualPathwayModel/DualPathwayModel.ipynb.
• To generate Fig4a, use Model/DualPathwayModel/RepeatRunswArtificial.ipynb.
• To generate Fig4b, use Model/Benchmarks/RepeatBenchmarkWSyrinx.ipynb.

1 dataset file:-

• SyrinxModel/Contour/Z-T03_P005_n10.npy: The performance landscape generated using the syrinx model and used to test the dual pathway model.

Usage

• To simulate the dual pathway model on any specific scenario, use the script Model/DualPathwayModel/DualPathwayModel.ipynb.
• To generate several simulations of the dual pathway model as a batch on Gaussian-based performance landscapes, use Model/DualPathwayModel/RepeatRunswArtificial.ipynb.
• To generate several simulations of the dual pathway model as a batch on Syrinx-based performance landscapes, use Model/DualPathwayModel/RepeatRunswSyrinx.ipynb.
• Model/DualPathwayModel/RepeatRunswArtificial.ipynb and Model/DualPathwayModel/RepeatRunswSyrinx.ipynb can also be used to compute summary statistics, either by first running several simulations, or using the Performance.npy file provided in each folder.
• The Performance.npy file provided in each folder contains the performance metric of batch simulations already run in the relevant scenarios.
• To simulate the alternate learniing rules on the benchmark framework, use Model/Benchmarks/BenchmarkModel.ipynb.
• To run the benchmark tests, use Model/Benchmarks/RepeatBenchmarkwArtificial.ipynb or Model/Benchmarks/RepeatBenchmarkWSyrinx.ipynb by specifying the desired learning rule.