
Neurosalience #S6E3 with Kendrick Kay - Philosophy, deep sampling, and the advancing tide of AI
“What does it actually mean to understand the brain?”
Dr. Kendrick Kay is a computational neuroscientist and neuroimaging expert at the University of Minnesota’s Center for Magnetic Resonance Research, where he is an Associate Professor in the Department of Radiology. With training spanning philosophy and neuroscience, from a bachelor’s degree in philosophy at Harvard University to a PhD in neuroscience from UC Berkeley, Dr. Kay’s work bridges deep theoretical questions with cutting-edge neuroimaging methods.
In this conversation, Peter Bandettini and Kendrick Kay explore the evolving landscape of neuroscience at the intersection of fMRI, philosophy, and artificial intelligence. They reflect on the limits of current neuroimaging methodologies, what fMRI can and cannot tell us about brain mechanisms, and why creativity and human judgment remain central to scientific progress. The discussion also dives into Dr. Kay’s landmark contributions to fMRI decoding and the Natural Scenes Dataset, a high-resolution resource that has become foundational for computational neuroscience and neuro AI research.
Along the way, they examine deep sampling in neuroimaging, individual variability in brain data, and the challenges of separating neural signals from hemodynamic effects. Framed by broader questions about understanding benchmarking progress, and the growing role of LLM’s in neuroscience, this wide-ranging conversation offers a thoughtful look at where the field has been and where it may be headed.
We hope you enjoy this episode!
Chapters:
00:00 - Introduction to Kendrick Kay and His Work
04:51 - Philosophy’s Influence on Neuroscience
17:17 - How Far Will fMRI Take Us?
23:27 - Understanding Attention in Neuroscience
30:00 - Science as a Process
34:17 - The Role of Large Language Models (LLMs) in Scientific Progress
38:29 - Why Humans Should Stay in the Equation
40:30 - Creativity vs. AI in Scientific Research
54:48 - Dr. Kay’s Natural Scenes Dataset (NSD)
01:00:27 - Deep Sampling: Considerations and Implications
01:08:00 - Accounting for biological variation in Brain Scans: Differences and Similarities
01:13:00 - Separating Hemodynamic Effects from Neural Effects
01:16:00 - Areas of Hope and Progress in the field
01:21:00 - How Should We Benchmark Progress?
01:22:59 - Advice for Aspiring Scientists
Works mentioned:
54:48 - https://www.nature.com/articles/s41593-021-00962-x
54:50 - https://www.sciencedirect.com/science/article/pii/S0166223624001838?via%3Dihub
Episode producers:
Xuqian Michelle Li, Naga Thovinakere
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