Special issue for Analytical Connectionism 2023 and 2024

We are pleased to announce the launch of a special issue for Analytical Connectionism, which will feature lecture notes from the 2023 and 2024 editions of the school. The issue will be published in the Proceedings of Machine Learning Research (PMLR), and all contributions will undergo a peer review process, resulting in a formal publication.

We encourage all participants of the 2023 and 2024 editions to reach out to other note-takers in their group and collaborate on submitting lecture notes. While there is no obligation to contribute-and some group members may not have the possibility to do so-this is a great opportunity to share what you’ve learned with future participants and to secure a publication in a respected venue.

The official special issue webpage can be found here: PMLR Volume 320.

The following lecture notes and papers have been included in this collection:

• Models of attractor dynamics in the brain by Tala Fakhoury, Elia Turner, Sushrut Thorat, and Athena Akrami.
• Thinking of Neural Networks Like a Physicist: The Statistical Physics of Machine Learning by Kai Jappe Sandbrink, Stefano Sarao Mannelli, and Florent Krzakala.
• An Introduction to Connectionist Theories of Semantic Cognition by Ari S Benjamin, Anna-Lea Beyer, Marianne De Heer Kloots, Jaedong Hwang, Hajer Karoui, Mitchell Ostrow, Jirko Rubruck, Kai Jappe Sandbrink, Satchel Grant, Andrew M Saxe, and James Lloyd McClelland.
• On the Impact of Representation Sharing on Parallel Processing in Neural Network Architectures by Maximilian Mittenbühler, Sven Wientjes, and Sebastian Musslick.
• Reinforcement learning: Computational modeling of learning and decision-making by Zach Cohen, Akshay Kumar Jagadish, Eghbal A. Hosseini, and Maria K Eckstein.
• Natural Image Statistics, Visual Representation, and Denoising by Imran Thobani, Alisa Leshchenko, and Eero P Simoncelli.
• Unifying neural population dynamics, manifold geometry, and circuit structure by Adam Manoogian, Asit Pal, Zachary Friedenberger, and Tatiana A Engel.
• The Statistics of Natural Experience by Dota Tianai Dong, Jing Li, Tobias Thomas, and Linda B. Smith.
• A Computational Basis of Natural Intelligence by Alireza Karami, Veronica Chelu, Po-Chen Kuo, Lindsay M. Smith, Mia Whitefield, and Jonathan D. Cohen.
• From Place Cells to Predictive Codes: Lecture Notes on the Dynamic Hippocampus by Mildred Salgado-Menez and Andre Fenton.

Submission platform

Submissions will be managed through OpenReview. All materials must be submitted via the following platform: OpenReview submission portal.

Template and formatting

Please use the provided submission template for formatting your document. The template can be accessed here: Submission template.

Page limit

There is no strict page limit for the submissions. However, we recommend the following approximate lengths:

• Long lectures: ~20 pages
• Shorter lectures: ~12 pages

These are only guidelines, and we understand that the length may vary depending on the content of each lecture.

Co-authorship

If you are submitting lecture notes on behalf of a lecturer, please ensure that the lecturer is included as a co-author. The lecture notes are their intellectual property, and their consent is necessary for the submission.

All submissions will go through a peer review process. You will be notified of the results of the review during the rebuttal and discussion period.

We have provided an updated LaTeX style file for the camera-ready version. Download the .sty file here.

To use it, simply include the style in your LaTeX preamble after \documentclass{article}:

\usepackage{analytical-connectionism-2023-2024}

Then, configure the edition details using the following commands:

\acedition{YYYY}
\aclecturer{LECTURER NAME}
\shorttitle{SHORT TITLE FOR HEADER}

Replace YYYY with the school year, LECTURER NAME, and SHORT TITLE FOR HEADER accordingly.

For reference, here is an example of a special issue completed by another school: Les Houches special issue.