Sparse coding and decorrelation in primary visual cortex during natural vision.

By William E. Vinje, Jack L. Gallant. 2000

1,403 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Representation & Self-Supervised Learning, Neuroscience

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Citations in the corpus

Feature Purification: How Adversarial Training Performs Robust Deep Learning (2020, methodology): “We use a data model called sparse coding, which is a popular model to model image, text and speech data [11, 12, 69, 77, 78, 100, 105, 106].”
Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “The visual system is characterised by a highly divergent projection from lateral geniculate nucleus to primary visual cortex (Stevens, 2001), and this is accompanied by considerable sparsening and decorrelation of activity (Olshausen and Field, 1996; Vinje and Gallant, 2000; Froudarakis et al., 2014”
Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex (2007, methodology): “The selectivity of each neuron across the fixed set of 213 stimuli was quantified by the sparseness of its response (Rolls and Tovee, 1995a; Vinje and Gallant, 2000; Olshausen and Field, 2004): S {1 [( Ri/n) 2/ (Ri 2/n)]}/[1 (1/n)], where Ri is the neuron response to the ith stimulus and n is the…”
Cortical feedback decorrelates olfactory bulb output in awake mice (2015, background): “Decorrelation of ensemble neuronal responses in early sensory circuits has been proposed as a mechanism for separation of similar input patterns by downstream circuits (Laurent, 2002; Vinje and Gallant, 2000; Wiechert et al., 2010).”
A Simple Connectivity Scheme for Sparse Coding in an Olfactory System (2007, background): “Studies in mammals (including humans), in songbirds, and in insects (Thompson and Best, 1989; Rolls and Tovee, 1995; Vinje and Gallant, 2000; Hahnloser et al., 2002; Laurent, 2002; PerezOrive et al., 2002, 2004; DeWeese et al., 2003; Olshausen and Field, 2004; Quian Quiroga et al., 2005) reveal…”
From learning models of natural image patches to whole image restoration (2011, background): “Sparse coding models have become extremely popular in recent years, both in the neuroscience community [15, 36, 37, 38] and the image processing and computer vision community [39, 40, 41, 42], From a generative perspective sparse coding can be modeled as:”
Odor representations in olfactory cortex: "sparse" coding, global inhibition, and oscillations. (2009, background): “Sparse representations of stimuli have been found across a variety of sensory systems (Brecht and Sakmann, 2002; Davison and Katz, 2007; Hahnloser et al., 2002; Hromadka et al., 2008; Margrie et al., 2002; Perez-Orive et al., 2002; Rinberg et al., 2006; Vinje and Gallant, 2000).”
Reassessing pattern separation in the dentate gyrus (2013, background): “In fact, sparse coding for visual processing has been illustrated in the primary visual cortex in macaques (Vinje and Gallant, 2000).”
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. (2016, positive): “Despite variability in estimates of the sparsity of a given brain region [27,153,156,157], the DG is widely believed to sustain among the sparsest neural code in the brain ( (cid:1) 0.5 – 1% population sparseness) [25 – 27].”
Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. (2006, background): “…as defaults because physiological signals are commonly considered to be sparsely distributed (Baddeley et al. 1997; Bell and Sejnowski 1997; Olshausen and Field 1996; Vinje and Gallant 2000) and corrupted by signal-dependent noise (Schmidt et al. 1979; Sutton and Sykes 1967; van Beers et al. 2004).”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, background): “In particular, it has been found that the selectivity and sparseness of individual neurons increase with the size of the stimulation area beyond the classical receptive field of the cell (Vinje and Gallant, 2000, 2002).”
Why does natural scene categorization require little attention? Exploring attentional requirements for natural and synthetic stimuli (2005, background): “Most of these studies, however, have been performed using artificial, computer-generated stimuli; yet the visual system might be optimized for processing the stimuli that it most frequently encounters in its natural environment (Vinje & Gallant, 2000).”