Overview
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 behaviours. 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 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.
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; the 2023 and 2024 notes are already published.
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, organisers, and other participants during a poster session;
- work in a group with other participants on a novel research project, mentored by the course organisers and lecturers.
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).
This year’s school will feature 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 processes;
- 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.
Dates
| Application deadline | 18 Apr 2025 |
| Outcome communicated | 25 May 2025 |
| Deadline to accept admission | 15 Jun 2025 |
| School begins | 25 Aug 2025 |
Schedule
| Time | Mon 8/25 | Tue 8/26 | Wed 8/27 | Thu 8/28 | Fri 8/29 |
|---|---|---|---|---|---|
| 09:00 | School intro | ||||
| 09:00–10:30 | Neural networks and cognitive neuroscience: What are models for? Tim Rogers | Credibility & misinformation Rani Moran | Rémi Monasson | Contemporary neural networks for engaging cognitive theories Tim Rogers | |
| 09:30–10:30 | Contributed talks | ||||
|
10:30–11:00
break
|
|||||
| 11:00–12:30 | Tutorial on random matrix theory (pt. 1) Flavio Nicoletti | Backpropagation and its application to modeling conceptual development Tim Rogers | Biases in information seeking Tali Sharot | Rémi Monasson | Understanding visual object representation with convolutional neural networks Tim Rogers |
|
12:30–13:30
lunch
|
|||||
| 13:30–15:00 | Tutorial on random matrix theory (pt. 2) Flavio Nicoletti | Overview & valence bias Tali Sharot | Rémi Monasson | Anatomically-constrained models for understanding disordered cognition Tim Rogers | Rémi Monasson |
|
15:00–15:30
break
|
|||||
| 15:30 | Contributed talks | Bias within reinforcement learning Stefano Palminteri | Rémi Monasson | AI & bias Moshe Glickman | Project organisation |
| 16:30–18:00 | Poster session | ||||
| 17:00–18:00 | Project brainstorms | Project brainstorms | Project brainstorms | ||
| 18:00– | School dinner | ||||
| Time | Mon 9/1 | Tue 9/2 | Wed 9/3 | Thu 9/4 | Fri 9/5 |
|---|---|---|---|---|---|
| 09:00–10:30 | Reinforcement learning in the brain Tiago Maia | Exploring just enough? An origin story of social stereotypes Xuechunzi Bai | On the biases and blessings of observational data (pt. 1) Fanny Yang | Merav Ahissar | Project work |
|
10:30–11:00
break
|
|||||
| 11:00–12:30 | Using computational psychiatry to develop a rigorous and integrative understanding of psychiatric disorders Tiago Maia | Aligning AI values with human values? Top down instructions vs. bottom-up training Xuechunzi Bai | On the biases and blessings of observational data (pt. 2) Fanny Yang | Mathew Diamond | Project work |
|
12:30–13:30
lunch
|
|||||
| 13:30–15:00 | Being biased before even seeing the data Marco Baity-Jesi | Lasana Harris | Angela Radulescu | Levent Sagun | Project presentations |
|
15:00–15:30
break
|
|||||
| 15:30 | Project work | Project work | Project work | Project work | Project presentations |
Lecturers
Core lecturers
Topic lecturers
Participants
Organisers
Practicalities
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 no more than 40 participants, 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 course fee of 600 GBP. Attendees are expected to cover their own travel, accommodation and other subsistence expenses. Lunch and coffee/tea breaks will be provided on course days, and there will be one course dinner.
Cancellation policy:
- Within 7 days of registering/making the booking: 100% refund
- After 7 days of registering/making the booking and up to 15 days before the start of the school: 80% refund
- Less than 15 days before the start of the school: no refund
Financial assistance via travel grant 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.
Frequently asked questions
- Will I receive individual feedback if my application is not successful?
Unfortunately, due to the large number of applicants, we are unable to offer individual feedback on unsuccessful applications.
- Other questions not covered in the FAQs?
Contact us at admissions@gatsby.ucl.ac.uk.
- I need a visa to attend the programme. Will you provide the necessary documents for my visa application if I am accepted?
If you require a UK Standard Visitor visa, we will provide an invitation letter to support your application once you confirm your acceptance into the programme. Please note that visa processing can take several weeks, so it’s necessary to start the application process as soon as you accept the offer.
- I am not based in London. Where can I stay during the programme?
You may wish to look into UCL Summer Residences or University of London halls, both open to the general public during the student vacation period in summer. The YMCA Indian Student Hostel (if eligible) is within five minutes walking distance. London Metropolitan University also has a list of summer accommodation options on its website.
- What are the requirements for poster presentations?
Due to space constraints, posters must be in A1 portrait format (594mm x 841mm).
- What are the requirements for spotlight presentations?
Presentations will be short, likely a maximum of 5 minutes. We will email final guidelines to all presenters closer to the programme start date.