Spatiotemporal elements of macaque v1 receptive fields.

By Nicole C. Rust, Odelia Schwartz, J. Anthony Movshon, Eero P. Simoncelli. 2005

508 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Deflecting Adversarial Attacks with Pixel Deflection (2018, background): “Many vision and neuroscience researchers [24, 36, 18] have suggested that the visual systems of many animals take advantage of these priors, as the simple cells in the primary visual cortex have been shown to have Gabor-like receptive fields.”
Spatio-temporal correlations and visual signalling in a complete neuronal population (2008, background): “[ 10 ] E. Schneidman, M. Berry, R. Segev, and W. Bialek.”
Stereopsis via deep learning (2013, background): “Their approach furthermore suggest that besides pooling, inhibitory connections may improve the estimates and thereby the performance of the model.”
Deep convolutional models improve predictions of macaque V1 responses to natural images (2017, methodology): “To address this issue, LN-LN cascade models have been proposed, 12 which either learn (convolutional) subunits (Rust et al., 2005; Touryan et al., 2005; Vintch et al., 2015) 13 or use handcrafted wavelet representations (Willmore et al.”
Spike-triggered neural characterization. (2006, methodology): “... systems ranging from the invertebrate motion system (Bialek & de Ruyter van Steveninck, 2005; Brenner, Bialek & de Ruyter van Steveninck, 2000 ;d e Ruyter van Steveninck & Bialek, 1988 )t o songbird forebrain auditory neurons (Sen, Wright, Doupe, & Bialek, 2000) to vertabrate retina cells (Pillo”
How MT cells analyze the motion of visual patterns (2006, background): “It emerges that conceptually similar variations in the parameters of a single underlying model can account for the range of behaviors from purely simple to purely comple”
A Canonical Neural Circuit for Cortical Nonlinear Operations (2008, methodology): “) and divisive normalization (Carandini & Heeger, 1994; Rust et al., 2005; Yu et al., 2002), we have arrived at a generalized nonlinear operation, equation 2.1, which can produce a wide range of nonlinear behaviors as shown in Figure 2.”
Do We Know What the Early Visual System Does? (2005, methodology): “Finally, the PSFT can capture the second-order mechanisms revealed by the Wiener (Emerson et al., 1987) and spike-triggered covariance methods (see above, Understanding V1 complex cells) (Rust et al., 2005; Touryan et al., 2005).”
Slowness and Sparseness Have Diverging Effects on Complex Cell Learning (2014, methodology): “We then applied the same analysis to the SFA filters as used in [42,41].”
Coding of color and form in the geniculostriate visual pathway (invited review). (2005, background): “4 remains useful even for complex cells— the spatial receptive fields in this representation correpond to the underlying linear filters; each complex cell combines several such filters.(139,165,172) This is of course merely a formal restatement of the hierarchical feedforward theory of cortical rece”
Diverse feature visualizations reveal invariances in early layers of deep neural networks (2018, unknown): “spike-triggered covariance [26]), our non-parametric approach could in principle also uncover other types of invariance that are not captured by quadratic features.”
Inhibition, spike threshold, and stimulus selectivity in primary visual cortex. (2008, unknown): “Suppressive filters tuned to the null direction have been identified in extracellular records from complex cells of the primate visual cortex, for example (Rust et al., 2005), although it is not yet clear how these suppressive filters are related to synaptic inhibition.”
A Structured Model of Video Reproduces Primary Visual Cortical Organisation (2009, background): “A second study [62] performed a similar analysis using spatio-temporal stimuli and found 2 to 8 significant dimensions for complex cells.”