2023 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 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.

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.

The course will run full-day Mondays-Fridays and end with a 1.5-day workshop, during which you will hear about current state-of-the-art and the limits of our understanding.

Applications to participate in the 2023 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 are no course fees, but attendees are expected to cover their own travel, accommodation and subsistence expenses.

Financial assistance may be available for successful applicants who find it difficult to take up a place for financial reasons. If funding becomes available, successful applicants will be asked to complete a financial aid request form if they need assistance. The amount of financial aid available will depend on the course funding from grants and sponsors.

Core lecturers

Topic lecturers

Panellists

Teaching assistants

TODO(stefsmlab) add some general details on the course content that will be covered.

Schedule

Contributed posters

1. Máté Aller, “Efficiency and (lack of) flexibility in a deep learning model of human spoken word recognition”
2. Jan Philipp Bauer, “Quantifying rich and robust inductive biases in chaotic recurrent neural networks”
3. Anna-Lea Beyer, “The relationship between behavioural tasks and brain space”
4. Victoria Bosche, “The brain can’t copy-paste: End-to-end topographic neural networks as a way forward for modelling cortical map formation and behaviour”
5. Chi-Ning Chou, “Probing biological and artificial neural networks with task-dependent neural manifolds”
6. Marianne de Heer Kloots, “What components of NLP models drive similarity to brain activity in language processing? Layer- and head-level analyses”
7. Mani Hamidi, “Using representation-learning to guide efficient exploration”
8. Michael Hanna, “Understanding subject-verb agreement in pre-trained language models: A circuits approach”
9. Eghbal Hosseini, “Teasing apart the representational spaces of ANN language models to discover key axes of model-to-brain alignment”
10. Jaedong Hwang, “Efficient exploration via fragmentation and recall”
11. Akshay Kumar Jagadish, “Using large-language models to meta-learn human inductive biases”
12. Maximilian Mittenbühler, “Human resource-rational planning: A neural network approach”
13. Turan Orujlu, “VividDreamer: Tokenized world model with stochastic attention”
14. Mitchell Ostrow, “Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamic mode representational similarity analysis”
15. Alexandra Proca, “Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks”
16. Safura Rashid Shomali, “Revealing hidden neuronal microcircuits from correlations among spiking neurons”
17. Jirko Rubruck, “Learning dynamics of semantic knowledge in humans and neural networks”
18. Ábel Ságodi, “An interpretable language for robust neural computation”
19. Quilee Simeon, “Dimensionality and dynamics of abstract representations”
20. Sushrut Thorat, “Characterising representation dynamics in recurrent neural networks for object recognition”
21. Elia Turner, “The simplicity bias in multi-task RNNs: Shared attractors, reuse of dynamics, and geometric representation”
22. Sven Wientjes, “Strategic cognitive control is bound to representations of temporal context”

Participant list

TODO(stefsmlab)

Course organizers