Jack Gallant
Helen Wills Neuroscience Institute
University of California, Berkeley

How is human visual system organized into functional areas, and what information is represented in each area? The best current technology for exploring these issues is functional MRI, but thus far fMRI has only provided coarse and patchy information about the functional organization of the human visual system. We have developed a new approach for collecting and modeling fMRI data that reveals the functional organization of the human visual system in much greater detail than was possible previously. Our approach is based on estimation of quantitative voxel-wise encoding models from fMRI responses evoked by natural movies. Projection of these encoding models onto flattened maps of individual human brains reveals a highly detailed and systematic representation of structural and semantic information distributed across wide swaths of visual and non-visual cortex. These patterns are consistent with the coarse parcellations provided by previous techniques, but provide much more detailed information and extend well beyond areas identified in earlier studies. Furthermore, examination of voxel-wise encoding models reveals what specific information is represented within each visual area, and suggests how the visual system exploits simple spatial and temporal features in order to construct semantic representations of objects and scenes. Finally, one additional benefit of our approach is that estimated encoding models can be easily converted into decoding models. These decoding models recover both the structural and semantic information in natural movies, even from slow hemodynamic signals.

Jack Gallant is Professor of Psychology at the University of California at Berkeley, and is affiliated with the graduate programs in Bioengineering, Biophysics, Neuroscience and Vision Science. He received his Ph.D. from Yale University and did post-doctoral work at the California Institute of Technology and Washington University Medical School. His research program focuses on constructing quantitative computational models that accurately describe how the brain represents information during natural tasks, and how these representations are modulation by attention. One interesting application of this computational modeling approach is to decode information in the brain in order to reconstruct mental experiences. Because this computational framework can be used to understand and decode brain activity measured by different methods and in different modalities, it has many potential applications in science and technology.

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