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236


Optimal denoising in redundant bases

Chapter by: Raphan, M.; Simoncelli, Eero P
in: Proceedings 2007 IEEE International Conference on Image Processing, ICIP 2007 by
Piscataway, NJ IEEE Service Center, 2007
pp. 113-116
ISBN: 978-1-4244-1436-9
CID: 371702

Image statistics and modeling

Simoncelli, Eero P
San Rafael : Morgan & Claypool, 2007
Extent: 1 v.
ISBN: 9781598292268
CID: 367602

Empirical Bayes Least Squares Estimation without an Explicit Prior

Raphan, Martin; Simoncelli, Eero P
[s.l.] : Courant Institute, 2007
Extent: 17 p.
ISBN: n/a
CID: 379352

A Bayesian Model of Conditioned Perception

Stocker, Alan A; Simoncelli, Eero P
We argue that in many circumstances, human observers evaluate sensory evidence simultaneously under multiple hypotheses regarding the physical process that has generated the sensory information. In such situations, inference can be optimal if an observer combines the evaluation results under each hypothesis according to the probability that the associated hypothesis is correct. However, a number of experimental results reveal suboptimal behavior and may be explained by assuming that once an observer has committed to a particular hypothesis, subsequent evaluation is based on that hypothesis alone. That is, observers sacrifice optimality in order to ensure self-consistency. We formulate this behavior using a conditional Bayesian observer model, and demonstrate that it can account for psychophysical data from a recently reported perceptual experiment in which strong biases in perceptual estimates arise as a consequence of a preceding decision. Not only does the model provide quantitative predictions of subjective responses in variants of the original experiment, but it also appears to be consistent with human responses to cognitive dissonance.
PMCID:4199208
PMID: 25328364
ISSN: 1049-5258
CID: 1931332

Optimal denoising in redundant bases

Chapter by: Raphan, Martin; Simoncelli, Eero P.
in: Proceedings - International Conference on Image Processing, ICIP by
[S.l.] : Neural information processing systems foundation, 2006
pp. ?-?
ISBN: 9781424414376
CID: 2872982

How MT cells analyze the motion of visual patterns

Rust, Nicole C; Mante, Valerio; Simoncelli, Eero P; Movshon, J Anthony
Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells
PMID: 17041595
ISSN: 1097-6256
CID: 112984

Spike-triggered neural characterization

Schwartz, Odelia; Pillow, Jonathan W; Rust, Nicole C; Simoncelli, Eero P
Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations
PMID: 16889482
ISSN: 1534-7362
CID: 143606

Quality-aware images

Wang, Zhou; Wu, Guixing; Sheikh, Hamid Rahim; Simoncelli, Eero P; Yang, En-Hui; Bovik, Alan Conrad
We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain
PMID: 16764291
ISSN: 1057-7149
CID: 143603

Sensory adaptation within a Bayesian framework for perception

Stocker, A.A.; Simoncelli, Eero P
ORIGINAL:0008281
ISSN: 1049-5258
CID: 371212

Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis

Pillow, Jonathan W; Simoncelli, Eero P
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties, namely, (1) it recovers a set of linear filters sorted according to their informativeness about the neural response; (2) it is both computationally efficient and robust, allowing recovery of multiple linear filters from a data set of relatively modest size; (3) it provides an explicit 'default' model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians; (4) it is equivalent to maximum likelihood estimation of this default model but also converges to the correct filter estimates whenever the conditions for the consistency of STA or STC analysis are met; and (5) it can be augmented with additional constraints on the filters, such as space-time separability. We demonstrate the effectiveness of the method by applying it to simulated responses of a Hodgkin-Huxley neuron and the recorded extracellular responses of macaque retinal ganglion cells and V1 cells
PMID: 16889478
ISSN: 1534-7362
CID: 143605