Thomas Naselaris, Ph.D.

Associate Professor, Department of Neuroscience


Research Interests:

The generative capabilities of the human visual system: Much of life is spent imagining or dreaming of internal images that one has never actually observed. Why is the human visual system so good at generating images, and how does this remarkable ability help us to see? We are addressing this question by monitoring the human brain as it engages complex, real-world scenery and as it calls upon memory to generate mental images. Using a combination of functional magnetic resonance imaging (fMRI), human intracranial EEG (iEEG), and mathematical modeling we aim to reveal the functional role of generative vision.

Modeling the brain’s response to the natural environment: Natural visual environments are a cluttered mess of spatially overlapping objects. How does the visual system identify, categorize, and localize the multitude of objects that crowd it’s visual field? We are developing predictive models that characterize visual responses to the many distinct kinds of information present in natural visual environments.


  • St-Yves G, Allen, EJ, Wu Y, Kay K*, & Naselaris T*. Brain-optimized neural networks  learn non-hierarchical models of representation in human visual cortex bioRxiv 2022.01.21.477293. 
  • Allen EJ, St-Yves G, Wu Y, Breedlove JL, Prince JS, Dowdle LT, Nau M, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T, Kay K. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat Neurosci. 2022 Jan;25(1):116-126. 
  • Mell MM, St-Yves G, Naselaris T. Voxel-to-voxel predictive models reveal unexpected structure in unexplained variance. Neuroimage. 2021 Sep;238:118266.
  • Naselaris T, Allen E,  Kay K. Extensive sampling for complete models of individual brains. Current Opinion in Behavioral Sciences, 40, 45-51. 
  • Allen EJ, St-Yves G, Wu Y, Breedlove J., Dowdle LT, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T*, Kay K* A massive 7T fMRI dataset to bridge cognitive and computational neuroscience. bioRxiv [PDF]
  • Breedlove J*, St-Yves G*, Olman CA, Naselaris T. Generative feedback explains distinct brain activity codes for seen and mental images. Current Biology, 30, 2211-2224. [PDF]
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