Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli.

By Valerio Mante, Vincent Bonin, Matteo Carandini. 2008

127 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

End-to-end Optimized Image Compression (2016, neutral): “Some earlier results suggest that incorporating local normalization in linear block transform coding methods can improve coding performance (Malo et al., 2006), and can improve object recognition performance of cascaded convolutional neural networks (Jarrett et al., 2009).”
How does the brain solve visual object recognition? (2012, neutral): “Such schemes were used originally to capture luminance and contrast andother adaptation phenomena in the LGNandV1 (Mante et al., 2008;Rust andMovshon, 2005), and they represent abroadclass of models, which we refer to here as the ‘‘normalized LN’’ model class (NLN; see Figure 5).”
Complex dynamics of V1 population responses explained by a simple gain-control model (2009, unknown): “Population Gain-Control Model The PGC model is a generalization of earlier single-neuron normalization models (Albrecht and Geisler, 1991; Heeger, 1991, 1992; Carandini and Heeger, 1994; Carandini et al., 1997; Mante et al., 2008).”
Perceptual image quality assessment using a normalized Laplacian pyramid (2016, background): “We also show that the resulting quality metric provides a better account of human perceptual judgements than either MS-SSIM or a recently-published gain-control metric based on oriented filters.”
Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization (2009, methodology): “In addition, we’ve shown that the optimal nonlinear radial transformation is similar to the gain control operations that have been used to model the response properties of mammalian visual neurons in retina (Shapley & Enroth-Cugell, 1984), LGN (Mante, Bonin, & Carandini, 2008), and primary visual co”
Eigen-Distortions of Hierarchical Representations (2017, unknown): “Following this linear stage, the outputs are normalized by two sequential stages of gain control, a known property of LGN neurons (Mante et al. [2008]).”
Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform (2010, background): “Blue dashed curves correspond to E(x1|x2) and E(x1|x2) ± std(x1|x2) from the optimally fitted multivariate t model to p(x1, x2). visual cortex [12] [19].”