2024 School on “Analytical Connectionism”

Overview

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 can help us unravel these “black boxes.”

This course will introduce (1) mathematical methods for neural-network analysis, providing a solid theoretical and analytical overview of the tools available to understand neural-network models, and (2) key connectionist models with links to experimental observations, and which provide important targets for analytical results. To provide context for the applications of these methods, we will discuss phenomena in psychology that still lack an explanation. During the 2-week course you will:

The course will run from August 12th to August 23rd, 2024, Monday to Friday, and will end with a 2-day hackathon, during which students will apply what they have learned in group projects.

Target audience

This course is appropriate for graduate students, postdoctoral fellows and faculty in a variety of fields, from psychology and neuroscience, to physics, computer science, and mathematics. Attendees are expected to have a strong background in one discipline and to have made some effort to introduce themselves to a complementary discipline.

The course is limited to ~38 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 populations underrepresented in the scientific workforce as defined by NIH, including but not limited to racially underrepresented individuals, women, individuals with disabilities and individuals from disadvantaged backgrounds.

Course Fees

There are no course fees, but attendees are expected to cover their own travel, visa expenses, and any meals not offered by the summer school (morning and afternoon coffee breaks and lunch will be provided Monday-Friday). Accommodation in NYC (for students not living in NYC and surrounding) will be provided by the school.

Travel grants inclusive of the above named personal expenses will be offered to individuals whose participation furthers the goal to promote diversity in systems and computational neuroscience, in particular among populations underrepresented in the scientific workforce as defined by NIH, including but not limited to racially underrepresented individuals, women, individuals with disabilities and individuals from disadvantaged backgrounds.

Outcome Notification

Organization

Lecturers

First week:

Second week:

Organizers

Course organizers

TAs

Project mentors

Course content

Schedule

To be detailed.

Contributed posters

To be detailed.