Motivation
This inaugural special issue for the School on Analytical Connectionism features lecture notes from the 2023 and 2024 editions of the school. The issue is published in the Proceedings of Machine Learning Research (PMLR), and all contributions underwent a peer review process, resulting in a formal publication.
Contents
The issue was published in April 2026 as Volume 320 of the Proceedings of Machine Learning Research, under the title Proceedings of the Analytical Connectionism Schools 2023–2024. It collects ten sets of peer-reviewed lecture notes written by participants of the 2023 and 2024 schools together with the lecturers whose material they cover, spanning attractor networks, the statistical physics of learning, semantic cognition, parallel processing, reinforcement learning, natural image statistics, neural population dynamics, the statistics of natural experience, natural intelligence, and hippocampal computation.
The notes are listed below in volume order; each entry links to its PMLR page and PDF.
- Models of attractor dynamics in the brain
- Thinking of neural networks like a physicist: the statistical physics of machine learning
- An introduction to connectionist theories of semantic cognition
- On the impact of representation sharing on parallel processing in neural network architectures
- Reinforcement learning: computational modeling of learning and decision-making
- Natural image statistics, visual representation, and denoising
- Unifying neural population dynamics, manifold geometry, and circuit structure
- The statistics of natural experience
- A computational basis of natural intelligence
- From place cells to predictive codes: lecture notes on the dynamic hippocampus
Curators
