Computational Visual Neuroscience Laboratory at CMRR
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Brain art

Current Projects

The lab has recently been pursuing the following research directions:

  • Developing pre-processing and visualization tools for ultra-high-resolution fMRI data
  • Investigating the effects of veins and sinuses on fMRI data and developing methods for correcting these artifacts
  • Understanding fine-scale activity patterns in high-level visual cortex
  • Characterizing the functional role of cortical layers with respect to bottom-up and top-down processing

Mission Statement

The Computational Visual Neuroscience Laboratory (CVNLAB) is located in the Center for Magnetic Resonance Research (CMRR) in the Radiology Department at the University of Minnesota. The goal of the lab is to understand how the human brain represents visual images and makes perceptual decisions about these images. We use a combined experimental and computational approach that seeks to develop models that characterize the stimulus transformations perfomed by the brain. Our primary measurement technique is functional magnetic resonance imaging (fMRI), which is ideally suited to identify these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. Recent increases in magnetic field strength (10.5T) are expected to provide substantial gains in spatial resolution and signal-to-noise ratio, enabling the acquisition of large, high-quality datasets that can be used to resolve functional differences across cortical layers and columns. In the spirit of reproducible research, we make freely available tools and resources (e.g. experiments, data, code) developed in the course of our research.

 What computations does the visual system perform? 



March 2018

October 2017

August 2017

February 2017

October 2016

  • Kendrick presents a talk at UW-Madison on ongoing work in developing signal processing techniques for obtaining accurate measurements of neural activity using ultra-high-resolution fMRI.

September 2016

  • Talk at Neurohackweek 2016 on data science, statistics, model-based fMRI, and high-res fMRI. See Resources page.

June 2016

May 2016

  • VSS talk on deep neural networks available. See Resources page.

 T2*-weighted functional image, GE-EPI, 0.8-mm isotropic, 2.2-s TR (click for full image) 




University of Minnesota | Department of Radiology | Center for Magnetic Resonance Research