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Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1
Rust, NC; Schwartz, O; Movshon, JA; Simoncelli, E
Neurons in primary visual cortex are commonly characterized using linear models, or simple extensions of linear models. Specifically, V1 simple cell responses are often characterized using a rectified linear receptive field, and complex cell responses are often described as the sum of squared responses of two linear subunits. We examined this class of model directly by applying spike-triggered covariance analysis to responses of monkey V1 neurons under binary white noise stimulation. The analysis extracts a low-dimensional subspace of the full stimulus space that is primarily responsible for generation of the neural response, including both excitatory and suppressive components. We found no fewer than two excitatory dimensions in simple cells, and as many as seven dimensions in complex cells. For all cells, we also found suppressive dimensions that were at least equal in number to the excitatory dimensions. These results suggest that extensions to standard models are required to fully describe the response properties of cells in V1. (C) 2004 Published by Elsevier B.V
ISI:000222245900115
ISSN: 0925-2312
CID: 98207
Local phase coherence and the perception of blur
Wang, Zhou; Simoncelli, Eero P
ORIGINAL:0008283
ISSN: 1049-5258
CID: 371232
Differentiation of discrete multidimensional signals
Farid, Hany; Simoncelli, Eero P
We describe the design of finite-size linear-phase separable kernels for differentiation of discrete multidimensional signals. The problem is formulated as an optimization of the rotation-invariance of the gradient operator, which results in a simultaneous constraint on a set of one-dimensional low-pass prefilter and differentiator filters up to the desired order. We also develop extensions of this formulation to both higher dimensions and higher order directional derivatives. We develop a numerical procedure for optimizing the constraint, and demonstrate its use in constructing a set of example filters. The resulting filters are significantly more accurate than those commonly used in the image and multidimensional signal processing literature
PMID: 15376584
ISSN: 1057-7149
CID: 143594
Image quality assessment: from error visibility to structural similarity
Wang, Zhou; Bovik, Alan Conrad; Sheikh, Hamid Rahim; Simoncelli, Eero P
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000
PMID: 15376593
ISSN: 1057-7149
CID: 143595
Local phase coherence and the perception of blur
Chapter by: Wang, Zhou; Simoncelli, Eero P.
in: Advances in Neural Information Processing Systems by
[S.l.] : Neural information processing systems foundation, 2004
pp. ?-?
ISBN: 9780262201520
CID: 2872912
Maximum likelihood estimation of a stochastic integrate-and-fire neural model
Chapter by: Pillow, Jonathan W.; Paninski, Liam; Simoncelli, Eero P.
in: Advances in Neural Information Processing Systems by
[S.l.] : Neural information processing systems foundation, 2004
pp. ?-?
ISBN: 9780262201520
CID: 2872922
Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics
Zhou Wang; Simoncelli, E.P.
INSPEC:8128733
ISSN: 0277-786x
CID: 367352
Local analysis of visual motion
Chapter by: Simoncelli, Eero P
in: The visual neurosciences by Werner, John Simon; Chalupa, Leo M [Eds]
Cambridge, Mass. : MIT Press, c2004
pp. 1616-1623
ISBN: 9780262033084
CID: 371092
Characterization of neural responses with stochastic stimuli
Chapter by: Simoncelli, Eero P; et al
in: The cognitive neurosciences by Gazzaniga, Michael S [Eds]
Cambridge, Mass. : MIT Press, c2004
pp. 327-338
ISBN: 9780262072540
CID: 367782
Multi-scale structural similarity for image quality assessment
Chapter by: Wang, Zhou; Simoncelli, Eero P.; Bovik, Alan C.
in: Conference Record of the Asilomar Conference on Signals, Systems and Computers by
[S.l.] : Neural information processing systems foundation, 2003
pp. 1398-1402
ISBN:
CID: 2872902