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.
Some areas of active research include understanding how early visual representations are transformed into high-level visual representations, understanding how top-down task demands affect representation, building predictive models for behavioral judgments, characterizing the effects of feedback on early visual representations, and developing denoising techniques for fMRI.
[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]
[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]
[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]fMRI data from "Attention reduces spatial uncertainty in human ventral temporal cortex" [NEW]
[Mapping of population receptive fields in face-selective regions in human visual cortex under different behavioral states]
[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]
Visual representations are dominated by intrinsic fluctuations correlated between areas. NeuroImage (2015). [NEW]
Attention reduces spatial uncertainty in human ventral temporal cortex. Current Biology (2015).
Computational modeling of responses in human visual cortex. In: Brain Mapping: An Encyclopedic Reference, edited by P. Thompson & K. Friston (2015).
Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex. Current Biology (2013).
Compressive spatial summation in human visual cortex. Journal of Neurophysiology (2013).
Bayesian reconstruction of natural images from human brain activity. Neuron (2009).
This is commentary on:
Identifying natural images from human brain activity. Nature (2008).
Evaluating the accuracy of diffusion MRI models in white matter. PLOS ONE (2015). [NEW]
Sparse Atomic Feature Learning via Gradient Regularization: With Applications to Finding Sparse Representations of fMRI Activity Patterns. IEEE SPMB (2014).
Evaluation and statistical inference for human connectomes. Nature Methods (2014).
GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Frontiers in Neuroscience (2013).
Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nature Medicine (2013).
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).
Modeling low-frequency fluctuation and
hemodynamic response timecourse in event-related fMRI. Human Brain Mapping (2008).
Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks. IEEE EMBS (2014).
Haptic fMRI: combining functional neuroimaging with haptics for studying the brain's motor control representation. IEEE EMBS (2013).
Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models. Annals of Applied Statistics (2011).
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).
Manuscripts in progress
Resolving ambiguities of MVPA using explicit models of representation.
Shallow-to-deep computational models explain the levels of representation in the visual hierarchy.
GLMdenoise improves multivariate pattern analysis of fMRI data.
Adaptive smoothing of fMRI data using Fast Gaussian Processes Regression.
2015-03, Cosyne (Computational and Systems Neuroscience) Workshop
2014-11, University of Glasgow, Institute of Neuroscience and Psychology
2014-09, Carnegie Mellon University, Psychology Department
2014-05, Vision Sciences Society (Symposium)
2013-05, MRC Cognition and Brain Sciences Unit
2013-04, MRC Cognition and Brain Sciences Unit
2008-11, Dartmouth, Psychology and
2008-11, Guest Lecture for Math
126 at Dartmouth
2008-03, Cosyne (Computational and
Systems Neuroscience) Workshop
2007-12, UC-Berkeley Brain Imaging
Center Research Day