School on Analytical Connectionism

Analytical Connectionism is a 2-week summer course on analytical tools, including methods from statistical physics and probability theory, for probing neural network models of higher-level cognition. The course brings together neuroscience, psychology and machine-learning communities, and introduces attendees to analytical methods for neural network analysis and connectionist theories of higher-level cognition and psychology.

Connectionism, a theoretical approach in cognitive science, uses neural network models to simulate a wide range of phenomena, including perception, memory, decision-making, language, and cognitive control. However, most connectionist models remain, to a certain extent, black boxes, and we lack a mathematical understanding of their behaviors. Recent progress in machine learning theory has provided novel analytical tools that have advanced our mathematical understanding of deep neural networks, and have the potential to help make these “black boxes” more transparent.

During the School, teams of students work closely to develop research projects on topics related to analytical connectionism, presenting initial proposals during week one and interim results at the School’s conclusion.

Additionally, students are grouped based on their expertise and preferences and assigned to take notes for a specific lecturer. These notes are peer-reviewed and collected into a special issue, creating a permanent resource for the community and giving note-takers the opportunity to contribute to a formal publication.

This course will introduce:

• mathematical methods for neural network analysis, providing a solid overview of the analytical tools available to understand neural network models;
• key connectionist models with links to experimental observations, which provide targets for analytical results.

During the course, you will:

• attend lectures given by leading researchers on theoretical methods and applications, key connectionist models, and experimental observations;
• participate in tutorials, poster sessions, and contributed talks;
• present to and engage with lecturers, organizers, and other participants during a poster session;
• collaborate in a group with other participants on a novel research project, mentored by the course organizers and lecturers;
• work together and co-author lecture notes for publication in a special issue.