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The Computational
Visual Neuroscience Laboratory (cvnlab)
is located at the Center for
Magnetic Resonance Research (CMRR) at the
University of Minnesota. We are a highly
collaborative lab that seeks to integrate broad
interdisciplinary insights to understand brain
function. If you are interested in collaborating
or working in the cvnlab, contact Kendrick and also
read the cvnlab
advising/mentoring/collaboration statement.
Fundamental questions of interest to cvnlab
include:
- Can we develop better models of
information processing in visual cortex?
- Can we exploit data-driven
approaches to gain insight into brain function?
- Can we build an integrated
understanding of how diverse stimuli and tasks
are processed in the visual system?
- Can we design techniques to
improve the quality and quantity of information
extracted from fMRI?
- Can we expand the
spatiotemporal limits of fMRI?
Research in the cvnlab
is vertically integrated --- spanning theory,
modeling, data, and analysis --- and lies at
intersection of three fields:
- Cognitive neuroscience:
We are interested in the representation and
processing of visual images by the brain. We
also study the anatomical and functional
topography of human cortex.
- Functional MRI methods:
We specialize in analysis methods for fMRI data.
This includes advanced statistical and analysis
methods (encoding, decoding, multivariate
analyses) as well as methods for improving the
quality of fMRI data (e.g. denoising methods).
We are currently interested in characterizing
and assessing ultra-high-resolution fMRI
measurements and developing associated analysis
methods.
- Computational
neuroscience: We use experimental
data to develop models of neural information
processing. We build functional (or
computational) models of information processing
in both low- and high-level cortex. We are also
interested in the principles of modeling and
associated philosophical issues.
The
cvnlab likes high-quality data, clear
and interpretable analyses, and
quantitative models. We are interested in
understanding the limitations of current
methodology and developing ways to overcome these
limitations. In the spirit of reproducible
research, we make freely available software tools
and data resources developed in the course of our
research.
Brain art
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News
October
2024
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July 2023
June 2023
T2*-weighted
functional image, GE-EPI, 0.8-mm isotropic,
2.2-s TR (click for full image)
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