Population receptive field estimates in human visual cortex

By Serge O. Dumoulin, Brian A. Wandell. NeuroImage, 2008

1,196 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4bc69d05b6d150e8399c0cf6946f810259caed01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Molecular Biology

Citations in the corpus

Visual field maps in human cortex. (2007, methodology): “This definition is fraught with controversy because these empirical criteria may conflict, and reasonable Figure 3.”
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014, background): “This allowed us to isolate different voxel groups, whose population RFs (pRFs) (Dumoulin and Wandell, 2008) are best predicted by a particular neural network layer.”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, background): “In other situations, G is a nonlinear function of free model parameters: For example, G 1014 depends non-linearly on the spatial tuning width in population receptive field modeling [57].”
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. (2008, background): “The receptive field size of visual cortical neurons is known to increase with eccentricity (Dumoulin and Wandell, 2008; Kay et al., 2008; Kraft et al., 2005; Smith et al., 2001), and in parallel, the cortical magnification factor decreases with eccentricity (Dougherty et al.”
Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience (2015, methodology): “…differences are unlikely to be due to lower neural signal in resting-state data because connective field size estimates are an unbiased statistic (Dumoulin and Wandell, 2008; Binda et al., 2013), the reliability of con- nective field size estimates will be affected by signal-to-noise, but the…”
Imaging retinotopic maps in the human brain (2010, methodology): “The field map locations and eccentricity map were measured with fMRI using pRF methods (Dumoulin & Wandell, 2008).”
Encoding and decoding in fMRI (2010, methodology): “On the encoding side, voxel-based encoding models (Dumoulin and Wandell, 2008; Gourtzelidis et al., 2005; Jerde et al., 2008; Kay et al., 2008; Mitchell et al., 2008; Naselaris et al., 2009; Schönwiesner and Zatorre, 2009; Thirion et al., 2006) have recently emerged as a promising computational tech”
Perceived Shape Similarity among Unfamiliar Objects and the Organization of the Human Object Vision Pathway (2008, background): “The selected voxels do not include the foveal confluence because of problems to reliably differentiate the retinotopic areas at this position (Dumoulin and Wandell, 2008); voxels on the border of areas were not included at all, and voxels were further constrained to be in gray matter.”
Reconstructing visual experiences from brain activity evoked by natural movies (2011, positive): “The spatial receptive fields of individual voxels were spatially localized (Figures 2F and 2G, left) and were organized retinotopically (Figures 2H and 2I), as reported in previous fMRI studies [12, 19–23].”
Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, methodology): “…for comparing internal representations in neural population codes between models and brains enable us to test models as theories of brain information processing (Dumoulin & Wandell 2008; Kay et al. 2008; Kriegeskorte et al. 2008a,b, 2013; Kriegeskorte 2011; Mitchell et al. 2008; Nili et al. 2014).”