Neural Data
The neural basis of cognition is not going to be solved by maths alone. We need rich behavioural data and flourishing collaborations between experimentally and theoretically minded folk. In this session, talks will explore new and exciting neural data—whether it be fMRI, electrophysiology, or otherwise—that may or may not yet have an explanation, with a particular focus on data that points to new computational paradigms in brain processing.
Neural Theory
While neuroscientists have increasingly powerful deep learning models that predict neural responses, it is not clear that these models are correspondingly increasing our understanding of what neurons are actually doing. In this session, we will take a more mechanistic approach to understanding how networks of neurons afford complex computations, both by considering mechanistic neural models along with mathematical theories that say how neurons should behave and crucially why they behave that way.
Cognitive Science
Design by Amey Zhang
How should an intelligent agent behave in order to best realize their goals? What inferences or actions should they make in order to solve an important computational task? Cognitive science aims to answer these questions at an abstract computational level, using tools from probability theory, statistical inference, and elsewhere.
In this session we will discuss how such optimal behavior should change under different conditions of uncertainty, background knowledge, multiple agents, or constraints on resource. This can be used to understand human behavior in the real world or the lab, as well as build artificial agents that learn robust and generalizable world models from small amounts of data.
Artificial Intelligence
Machine learning and artificial intelligence (AI) aim to create algorithms that solve difficult problems and simulate complex intelligent behavior. Many of these algorithms are based on findings and theory from the study of the brain and mind.
Recent rapid advances in these fields have seen the creation of algorithms and agents that can—finally—solve complex real-world problems across a wide range of domains. What are these advances, and how can we take them further? What remains beyond their capacity, and how can we overcome that? What might forever lie beyond their capabilities—or will anything?