Natural signal statistics and sensory gain control

By Odelia Schwartz, Eero P. Simoncelli. 2001

918 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neuroscience, Signal Processing

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Citations in the corpus

Group Normalization (2018, background): “In fact, a well-accepted computational model in neuroscience is to normalize across the cell responses [21, 52, 55, 5], “with various receptive-field centers (covering the visual field) and with various spatiotemporal frequency tunings” (p183, [21]); this can happen not only in the primary visual co”
End-to-end Optimized Image Compression (2016, background): “These may be signiﬁcantly reduced through the use of joint local nonlinear gain control operations (Schwartz and Simoncelli, 2001; Lyu, 2010; Sinz and Bethge, 2013), inspired by models of visual neurons (Heeger, 1992; Carandini and Heeger, 2012).”
Density Modeling of Images using a Generalized Normalization Transformation (2015, methodology): “Variants of divisive normalization have been found to reduce dependencies when applied to natural images or sounds and to produce approximately Gaussian responses (Ruderman, 1994; Schwartz & Simoncelli, 2001; Malo & Laparra, 2010).”
Barlow Twins: Self-Supervised Learning via Redundancy Reduction (2021, background): “…organization of the visual system, from the retina to cortical areas (see (Barlow, 2001) for a review and (Lindsey et al., 2020; Ocko et al., 2018; Schwartz & Simoncelli, 2001) for recent efforts), and has led to a number of algorithms for supervised and unsupervised learning (Ball´e et al.,…”
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes (2016, background): “In this section we ﬁrst review related work on normalization, followed by a brief description of regularization in neural networks.”
Value-based gain control: divisive normalization in parietal cortex (2011, methodology): “…divisive inhibition has been shown to increase the independence of neural responses and allow for efficient encoding of sensory signals (Schwartz and Simoncelli, 2001) and to enhance the stability of recurrent neural networks (Chance and Abbott, 2000), leading to the idea that divisive…”
The Statistical Computation Underlying Contrast Gain Control (2006, positive): “Contrast gain control is thought to help map the distribution of contrasts in natural scenes onto a limited range of firing rates (Laughlin, 1981; Tadmor and Tolhurst , 2000; Schwartz and Simoncelli, 2001).”
Contrast Gain Control in Auditory Cortex (2011, neutral): “is possible that this preferred distribution is defined by the ensemble of natural sounds (Attneave, 1954; Barlow, 1961; Schwartz and Simoncelli, 2001; Lewicki, 2002).”
Image Quality Assessment: From Error Measurement to Structural Similarity (2004, background): “Statistically, it has been shown that a well-designed nonlinear gain control model, in which parameters are optimized to reduce dependencies rather than for fitting data from masking experiments, can greatly reduce the dependencies of the transform coefficients [43], [44].”
Choice: Towards a Standard Back-pocket Model (2009, background): “Previous studies have noted that other (and perhaps all) cortical areas perform a divisive normalization on their input data ( Heeger, 1992 ; Schwartz and Simoncelli, 2001 ).”
Translation insensitive image similarity in complex wavelet domain (2005, methodology): “... information in primary visual cortex using quadrature pairs of localized bandpass filters [9]; 3) The computation of complex-valued product in visual cortex [10]; 4) The computation of local energy (using sums of squared responses of quadrature-pair filters) by complex cells in visual cortex [11”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, background): “Independence in Neural Signal Interestingly, Schwartz and Simoncelli have shown in a simulation that a similar divisive normalization scheme can achieve more independence in the neural signals, despite the dependences present in the natural image statistics [Schwartz and Simoncelli, 2001].”
Bilinear Sparse Coding for Invariant Vision (2005, methodology): “A related approach is presented by Tenenbaum and Freeman (2000) in their article dealing with style and content separation.”
Suppression without inhibition in visual cortex. (2002, background): “Indeed, early conclusions (Carandini and Heeger, 1994; Carandini et al., 1997; drawn from similar experiments (Sillito, 1975) have later Heeger, 1992; Schwartz and Simoncelli, 2001). been challenged (Nelson et al., 1994).”
Complex Wavelet Structural Similarity: A New Image Similarity Index (2009, background): “energy of neighboring filter responses) in both visual and auditory neurons [42], [43].”