Kendrick Kay
 kendrick.kay@wustl.edu

I am a Visiting Scholar in the Psychology Department at Washington University in St. Louis. My primary research interest is using computational models to understand how visual images are processed and represented in human visual cortex. Besides vision, I am also interested in fMRI methods, statistics and machine learning, and high-performance computing. Here is my CV.

Videos

    Vision Sciences Society 2014 Symposium: Understanding representation in visual cortex: why are there so many approaches and which is best?

[This is a collection of videos of short talks given by Thomas Naselaris, Marcel van Gerven, Kendrick Kay, Jeremy Freeman, Nikolaus Kriegeskorte, and Jim DiCarlo]

Code

    analyzePRF

[Stimuli and code for estimating population receptive fields and performing retinotopic mapping]
    GLMdenoise
[MATLAB toolbox for denoising task-based fMRI data]
    Repository of MATLAB helper functions, including
        some useful code for fitting nonlinear models

[A collection of MATLAB tools]

Public data

    fMRI natural image dataset (hosted by crcns.org)

[Measurements of responses in visual cortex to a large number of natural images]
    Models of BOLD responses in visual cortex (data and code)
[Measurements of responses in visual cortex to a wide range of synthetic stimuli; also includes code implementing a specific computational model]
    Denoise Benchmark for task-based fMRI
[A large collection of fMRI data under various visual stimulation protocols; also includes code comparing various denoising methods]

Teaching

    Statistics and Data Analysis in MATLAB [Psych 5007]

[Includes lecture slides, videos, code]
    Blog on statistical analyses in MATLAB
[Some statistical commentary with accompanying code]
    Cognitive neuroscience [Psych 3604]
[A collection of links to demos and materials available on the web]

Publications

Visual cortex

Computational modeling of responses in human visual cortex. In: Brain Mapping: An Encyclopedic Reference, edited by P. Thompson & K. Friston (in press).
     Wandell, B.A., Winawer, J., & Kay, K.N.
     PDF

Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex. Current Biology (2013).
     Winawer, J., Kay, K.N., Foster, B.L., Rauschecker, A.M., Parvizi, J., & Wandell, B.A.
     Pubmed link | Journal link | PDF (Main text) | PDF (Supplemental Information)

A two-stage cascade model of BOLD responses in human visual cortex. PLoS Computational Biology (2013).
     Kay, K.N., Winawer, J., Rokem, A., Mezer, A., & Wandell, B.A.
     Pubmed link | Journal link | PDF

Compressive spatial summation in human visual cortex. Journal of Neurophysiology (2013).
     Kay, K.N., Winawer, J., Mezer, A., & Wandell, B.A.
     Pubmed link | Journal link | PDF (Main text) | PDF (Supporting Information)

Bayesian reconstruction of natural images from human brain activity. Neuron (2009).
     Naselaris, T., Prenger, R.J., Kay, K.N., Oliver, M. & Gallant, J.L.
     Pubmed link | Journal link | PDF (Main text) | PDF (Supplementary Information)

I can see what you see. Nature Neuroscience (2009).
     Kay, K.N. & Gallant, J.L.
     Pubmed link | Journal link | PDF

This is commentary on:
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron (2008).
     Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M.A., Morito, Y., Tanabe, H.C., Sadato, N. & Kamitani, Y.
     Pubmed link | Journal link | PDF

Identifying natural images from human brain activity. Nature (2008).
     Kay, K.N., Naselaris, T., Prenger, R.J. & Gallant, J.L.
     Pubmed link | Journal link | PDF (Main text) | PDF (Supplementary Information)

See commentary:
What's in your mind. Nature Neuroscience (2008).
     Wandell, B.A.
     Pubmed link | Journal link | PDF

Topographic organization in and near human visual area V4. The Journal of Neuroscience (2007).
     Hansen, K.A., Kay, K.N. & Gallant, J.L.
     Pubmed link | Journal link | PDF

Methods

GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Frontiers in Neuroscience (2013).
     Kay, K.N., Rokem, A., Winawer, J., Dougherty, R.F. & Wandell, B.A.
     Pubmed link | Journal link | PDF

Encoding and decoding in fMRI. NeuroImage (2011).
     Naselaris, T., Kay, K.N., Nishimoto, S. & Gallant, J.L.
     Pubmed link | Journal link | PDF

Understanding visual representation by developing receptive-field models. In: Visual Population Codes: Towards a Common Multivariate Framework for Cell Recording and Functional Imaging, edited by N. Kriegeskorte & G. Kreiman (2011).
     Kay, K.N.
     Book link
| PDF

Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI. Human Brain Mapping (2008).
     Kay, K.N., David, S.V., Prenger, R.J., Hansen, K.A. & Gallant, J.L.
     Pubmed link | Journal link | PDF

Other

Evaluation and statistical inference for human connectomes. Nature Methods (2014).
     Pestilli, F., Yeatman, J.D., Rokem, A., Kay, K.N., Wandell, B.A.
     Pubmed link | Journal link | PDF | PDF (Supplementary Information)

Haptic fMRI: Accurately Estimating Neural Responses in Motor, Pre-Motor, and Somatosensory Cortex During Complex Motor Tasks. IEEE EMBS (2014).
     Menon, S., Brantner, G., Aholt, C., Kay, K., & Khatib, O.

Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nature Medicine (2013).
     Mezer, A., Yeatman, J.D., Stikov, N., Kay, K.N., Cho, N.-J., Dougherty, R.F., Perry, M.L., Parvizi, J., Hua, L.H., Butts-Pauly, K., & Wandell, B.A.
     Pubmed link | Journal link | PDF

Haptic fMRI: combining functional neuroimaging with haptics for studying the brain's motor control representation. IEEE EMBS (2013).
     Menon, S., Brantner, G., Aholt, C., Kay, K., & Khatib, O.
     Pubmed link | Journal link | PDF

Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models. Annals of Applied Statistics (2011).
     Vu, V.Q., Ravikumar, P., Naselaris, T., Kay, K.N., Gallant, J.L. & Yu, B.
     Pubmed link | Journal link | PDF

Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. In: Advances in Neural Information Processing Systems 21, edited by D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (2009).
     Ravikumar, P., Vu, V.Q., Yu, B., Naselaris, T., Kay, K.N. & Gallant, J.L.
     Book link | PDF

Manuscripts in progress

Attention reduces spatial uncertainty in human ventral temporal cortex. (submitted).
     Kay, K.N., Weiner, K.S., & Grill-Spector, K.

A framework for evaluating the accuracy of diffusion models. (submitted).
     Rokem, A., Yeatman, J.D., Pestilli F., Kay K.N., Mezer, A., Van der Walt, S., & Wandell, B.A.

Intrinsic cortical dynamics dominate population responses to natural images across human visual cortex. (submitted).
     Henriksson, L., Khaligh-Razavi, S., Kay, K., Kriegeskorte, N.

Reward motivation enhances task coding in frontoparietal cortex. (submitted).
     Etzel, J.A., Cole, M.W., Zacks, J.M., Kay, K.N., Braver, T.S.

Statistical smoothing of fMRI data using fast Gaussian Processes regression. (in preparation).
     Gilboa, E., Strappini, F., Kay, K., Pitzalis, S., Cunningham, J.P., Nehorai, A., Shulman, G.L., Corbetta, M., Snyder, A.

Invited Talks

2014-09, Carnegie Mellon University, Psychology Department
     How the brain builds high-level representations of visual stimuli

2014-05, Vision Sciences Society (Symposium)
     Identifying the nonlinearities used in extrastriate cortex

2013-05, MRC Cognition and Brain Sciences Unit
     GLMdenoise: a fast, automated technique for denoising task-based fMRI data

2013-04, MRC Cognition and Brain Sciences Unit
     A two-stage cascade model of BOLD responses in human visual cortex

2008-11, Dartmouth, Psychology and Brain Sciences
     Using computational models of voxels to identify images seen by an observer

2008-11, Guest Lecture for Math 126 at Dartmouth
     Building computational models of V1 voxels & Mathematical details of estimating receptive-field models

2008-03, Cosyne (Computational and Systems Neuroscience) Workshops
     Using voxel receptive field models to identify natural images seen by an observer

2007-12, UC-Berkeley Brain Imaging Center Research Day
     Building a general decoder for human visual cortex