Joss

.github/workflows/draft-pdf.yml

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+name: joss
+on:
+  push:
+    branches: 
+      - joss
+  pull_request:
+    branches: 
+      - joss
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: joss_paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: joss_paper/paper.pdf

joss_paper/paper.md

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+---
+title: 'Plenoptic.py: Synthesizing model-optimized visual stimuli'
+tags:
+  - Python
+  - PyTorch
+  - neural networks
+  - computational neuroscience
+  - image synthesis
+authors:
+  - name: Kathryn Bonnen
+    equal-contrib: true
+    orcid: 0000-0002-9210-8275
+    affiliation: 1
+  - name: William F. Broderick
+    equal-contrib: true
+    orcid: 0000-0002-8999-9003
+    affiliation: 2
+  - name: Lyndon R. Duong
+    equal-contrib: true
+    orcid: 0000-0003-0575-1033
+    affiliation: 2
+  - name: Pierre-Etienne Fiquet
+    equal-contrib: true
+    orcid: 0000-0002-8301-2220
+    affiliation: 2
+  - name: Nikhil Parthasarathy
+    equal-contrib: true
+    orcid: 0000-0003-2572-6492
+    affiliation: 2
+  - name: Thomas E. Yerxa
+    equal-contrib: true
+    orcid: 0000-0003-2687-0816
+    affiliation: 2
+  - name: Xinyuan Zhao
+    equal-contrib: true
+    affiliation: 2
+  - name: Eero P. Simoncelli
+    orcid: 000-0002-1206-527X
+    affiliation: "2, 3"
+affiliations:
+ - name:  School of Optometry, Indiana University, Bloomington, IN, USA
+   index: 1
+ - name: Center for Neural Science, New York University, New York, NY, USA
+   index: 2
+ - name: Center for Computational Neuroscience, Flatiron Institute, New York, NY, USA
+   index: 3
+date: January 2023
+bibliography: references.bib
+---
+
+# Summary
+
+In sensory perception and neuroscience, new computational models are most often tested and compared in terms of their ability to fit existing data sets.
+However, experimental data are inherently limited in size, quality, and type, and complex models often saturate their explainable variance.
+Moreover, it is often difficult to use models to guide the development of future experiments.
+Here, building on ideas for optimal experimental stimulus selection  (e.g., QUEST, Watson and Pelli, 1983), we present "Plenoptic", a python software library for generating visual stimuli optimized for testing or comparing models.
+Plenoptic provides a unified framework containing four previously-published synthesis methods -- model metamers (Freeman and Simoncelli, 2011), Maximum Differentiation (MAD) competition (Wang and Simoncelli, 2008), eigen-distortions (Berardino et al. 2017), and representational geodesics (Hénaff and Simoncelli, 2015) -- each of which offers visualization of model representations, and generation of images that can be used to experimentally test alignment with the human visual system.
+Plenoptic leverages modern machine-learning methods to enable application of these synthesis methods to any computational model that satisfies a small set of common requirements.
+The most important of these is that the model must be image-computable, implemented in PyTorch (Paszke et al. 2019), and end-to-end differentiable.
+The package includes examples of several low- and mid-level visual models, as well as a set of perceptual quality metrics.
+Plenoptic is open source, tested, documented, and extensible, allowing the broader research community to contribute new examples and methods.
+In summary, Plenoptic leverages machine learning tools to tighten the scientific hypothesis-testing loop, facilitating investigation of human visual representations.
+
+# Statement of need
+
+# Acknowledgements
+
+EPS and KB were funded by Simons Institute.
+
+For a quick reference, the following citation commands can be used:
+
+- `@author:2001`  ->  "Author et al. (2001)"
+- `[@author:2001]` -> "(Author et al., 2001)"
+- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)"
+
+# References
+
+@berardino_eigen-distortions_2017
+@henaff_geodesics_2015
+@simoncelli_steerable_1995
+@freeman_metamers_2011
+@wang_maximum_2008
+@paszke_pytorch_2019
+@portilla_parametric_2000

joss_paper/references.bib

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+
+@inproceedings{simoncelli_steerable_1995,
+	author = {{Simoncelli}, Eero P. and {Freeman}, William T.},
+	title = {The steerable pyramid: a flexible architecture for multi-scale derivative computation},
+	volume = {3},
+	isbn = {0-7803-3122-2},
+	doi = {10.1109/ICIP.1995.537667},
+	booktitle = {Proceedings., International Conference on Image Processing},
+	publisher = {IEEE Comput. Soc. Press},
+	year = {1995},
+	pages = {444--447},
+}
+
+@article{henaff_geodesics_2016,
+       author = {{H{\'e}naff}, Olivier J. and {Simoncelli}, Eero P.},
+        title = "{Geodesics of learned representations}",
+      journal = {International Conference on Learning Representations},
+         year = 2016,
+          eid = {arXiv:1511.06394},
+}
+
+@article{berardino_eigen-distortions_2017,
+	title = {Eigen-Distortions of Hierarchical Representations},
+	journal = {Adv. Neural Information Processing Systems},
+	author = {Berardino, Alexander and Ballé, Johannes and Laparra, Valero and Simoncelli, Eero P.},
+	month = dec,
+	year = {2017},
+	pages = {3530-3539},
+	keywords = {Computer Science - Computer Vision and Pattern Recognition},
+}
+
+@article{freeman_metamers_2011,
+    author = {{Freeman}, Jeremy and {Simoncelli}, Eero P.},
+	title = {Metamers of the ventral stream},
+	volume = {14},
+	issn = {1097-6256, 1546-1726},
+	doi = {10.1038/nn.2889},
+	language = {en},
+	number = {9},
+	urldate = {2019-09-25},
+	journal = {Nature Neuroscience},
+	month = sep,
+	year = {2011},
+	pages = {1195--1201},
+}
+
+@article{paszke_pytorch_2019,
+    author = {{Paszke}, Adam and {Gross}, Sam and {Massa}, Francisco and {Lerer}, Adam and {Bradbury}, James and {Chanan}, Gregory and {Killeen}, Trevor and {Lin}, Zeming and {Gimelshein}, Natalia}
+	title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
+	language = {en},
+	year = {2019},
+	pages = {12},
+}
+
+@article{wang_maximum_2008,
+	author = {{Wang}, Zhou and {Simoncelli}, Eero P.},
+	title = {Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities},
+	volume = {8},
+	issn = {1534-7362},
+	shorttitle = {Maximum differentiation (MAD) competition},
+	doi = {10.1167/8.12.8},
+	language = {en},
+	number = {12},
+	journal = {Journal of Vision},
+	month = sep,
+	year = {2008},
+	pages = {1-13},
+}
+
+@article{portilla_parametric_2000,
+	author = {{Portilla}, Javier and {Simoncelli}, Eero P.},
+	title = {A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients},
+	language = {en},
+	journal = {International Journal of Computer Vision},
+	year = {2000},
+	pages = {49-71},
+}