Searched for: in-biosketch:yes
person:eps2
Noise characteristics and prior expectations in human visual speed perception
Stocker, Alan A; Simoncelli, Eero P
Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities
PMID: 16547513
ISSN: 1097-6256
CID: 143601
How MT cells analyse the motion of visual patterns [Meeting Abstract]
Movshon, JA; Rust, NC; Mante, V; Simoncelli, EP
ISI:000243599300039
ISSN: 0301-0066
CID: 98052
Nonlinear image representation for efficient perceptual coding
Malo, Jesus; Epifanio, Irene; Navarro, Rafael; Simoncelli, Eero P
Image compression systems commonly operate by transforming the input signal into a new representation whose elements are independently quantized. The success of such a system depends on two properties of the representation. First, the coding rate is minimized only if the elements of the representation are statistically independent. Second, the perceived coding distortion is minimized only if the errors in a reconstructed image arising from quantization of the different elements of the representation are perceptually independent. We argue that linear transforms cannot achieve either of these goals and propose, instead, an adaptive nonlinear image representation in which each coefficient of a linear transform is divided by a weighted sum of coefficient amplitudes in a generalized neighborhood. We then show that the divisive operation greatly reduces both the statistical and the perceptual redundancy amongst representation elements. We develop an efficient method of inverting this transformation, and we demonstrate through simulations that the dual reduction in dependency can greatly improve the visual quality of compressed images
PMID: 16435537
ISSN: 1057-7149
CID: 143600
Image denoising with an orientation-adaptive Gaussian scale mixture model
Chapter by: Hammond, D.K.; Simoncelli, Eero P
in: 2006 International Conference on Image Processing by
Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2006
pp. ?-?
ISBN: 1-4244-0481-9
CID: 371732
Translation insensitive image similarity in complex wavelet domain
Chapter by: Wang, Zhou; Simoncelli, Eero P.
in: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings by
[S.l.] : Neural information processing systems foundation, 2005
pp. ?-?
ISBN: 9780780388741
CID: 2872962
Sensory adaptation within a Bayesian framework for perception
Chapter by: Stocker, Alan A.; Simoncelli, Eero P.
in: Advances in Neural Information Processing Systems by
[S.l.] : Neural information processing systems foundation, 2005
pp. 1289-1296
ISBN: 9780262232531
CID: 2872952
Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model
Pillow, Jonathan W; Paninski, Liam; Uzzell, Valerie J; Simoncelli, Eero P; Chichilnisky, E J
Sensory encoding in spiking neurons depends on both the integration of sensory inputs and the intrinsic dynamics and variability of spike generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrate-and-fire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance
PMID: 16306413
ISSN: 1529-2401
CID: 143599
Spatiotemporal elements of macaque v1 receptive fields
Rust, Nicole C; Schwartz, Odelia; Movshon, J Anthony; Simoncelli, Eero P
Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction
PMID: 15953422
ISSN: 0896-6273
CID: 112992
Comparing integrate-and-fire models estimated using intracellular and extracellular data [Meeting Abstract]
Paninski, L; Pillow, J; Simoncelli, E
ISI:000229663600048
ISSN: 0925-2312
CID: 367332
Machine learning applied to perception : decision images of classifications
Wichmann, F; Graf, A; Simoncelli, Eero P; Bulthoff, H; Scholkopf, B
ORIGINAL:0008282
ISSN: 1049-5258
CID: 371222