RĂ©mi Monasson
CNRS & Ecole Normale Supérieure
Overview
🚧 The 2025 School website is under construction.
Details on this page are subject to change until February 2025.
Details on this page are subject to change until February 2025.
Analytical Connectionism is a 2-week summer course on analytical tools, including methods from statistical physics and probability theory, for probing neural networks and 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 key theoretical approach in psychology, 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 theoretical neuroscience and machine learning 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 with faculty and postdoc mentors to develop research projects on topics related to analytical connectionism, presenting initial proposals during week one and interim results at the School’s conclusion. Participant projects from prior Schools have led to publications at NeurIPS.
đź“š This year's School will have a topical focus on "bias in learning."
What makes a learning system biased toward certain strategies? This year's School will examine case studies of deficits due to architectural or processing constraints alongside biases acquired as a consequence of statistical patterns in data.
In addition to this topical focus, attendees can expect to be introduced to tools and phenomena central to analytical connectionism (i.e., participants with no prior attendance of a School are encouraged to apply).
What makes a learning system biased toward certain strategies? This year's School will examine case studies of deficits due to architectural or processing constraints alongside biases acquired as a consequence of statistical patterns in data.
In addition to this topical focus, attendees can expect to be introduced to tools and phenomena central to analytical connectionism (i.e., participants with no prior attendance of a School are encouraged to apply).
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, Q&A sessions, and panel discussions;
- present to and engage with lecturers, organizers, and other participants during a poster session;
- work in a group with other participants on a novel research project, mentored by the course organizers and lecturers.
Important dates
- Applications open:
- February 1, 2025
- Application deadline:
- April 18, 2025
- Outcome communicated:
- May 18, 2025
- Deadline to accept admission:
- June 1, 2025
Application details
Applications to participate in the 2025 School on Analytical Connectionism are now closed.
Target audience
This course is appropriate for graduate students, postdoctoral fellows and early-career faculty in a number of fields, including psychology, neuroscience, physics, computer science, and mathematics. Attendees are expected to have a strong background in one of these disciplines and to have made some effort to introduce themselves to a complementary discipline.
The course is limited to 40 attendees, who will be chosen to balance the representation of different fields. In circumstances where all other things are equal, priority will be given to applicants from underrepresented groups in STEM fields, using positive action under the UK Equality Act 2010 where appropriate.
Course fees
There is a nominal course fee of 250 GBP to cover lunch and coffee breaks. Attendees are expected to cover their own travel, accommodation and other subsistence expenses.
Financial assistance via bursary may be available for successful applicants who find it difficult to take up a place for financial reasons. Applicants are asked to indicate in their application if they would like to be considered for financial aid. The amount of financial aid available will depend on the course funding from grants and sponsors.
Lecturers
Core lecturers
Topic lecturers
Course Content
This year, we are thrilled to have lecturers with expertise in the following research areas:
- analytical frameworks for understanding bias from statistical physics;
- perceptual and cognitive biases in human decision-making and belief formation;
- individual differences and deficits in learning, including atypical development and atypical cognitive proces
- social learning and the emergence of group-level stereotypes;
- bias amplification in machine learning systems from theoretical and applied perspectives;
- clinical applications in computational psychiatry and mental health.
These components will be delivered as a set of core lectures in the first week, followed by a set of topic lectures in the second week.
Participants
Contributed posters
To be announced in July 2025.
Participant list
To be announced in July 2025.
Organizers
Course organizers
Sponsors
This summer course is made possible by the generous support of and the Gatsby Computational Neuroscience Unit (funded by the Gatsby Charitable Foundation).
