Power spectra for ocular drift and tremor

Eizenman, Moshe; Hallett, Peter; Frecker, Richard C.

doi:10.1016/0042-6989(85)90134-8

Cited by 106 publications

(97 citation statements)

References 13 publications

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“…Performance under conditions of human vision. With D set to 100 arcmin 2 /s, corresponding to the measured statistics of human fixational drift (11)(12)(13), the factorized decoder performs well on images that cover at least 40 × 40 pixels (20 × 20 arcmin) (Fig. 3B).…”

Section: Resultsmentioning

confidence: 99%

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Bayesian model of dynamic image stabilization in the visual system

Burak

Rokni

Meister

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Humans can resolve the fine details of visual stimuli although the image projected on the retina is constantly drifting relative to the photoreceptor array. Here we demonstrate that the brain must take this drift into account when performing high acuity visual tasks. Further, we propose a decoding strategy for interpreting the spikes emitted by the retina, which takes into account the ambiguity caused by retinal noise and the unknown trajectory of the projected image on the retina. A main difficulty, addressed in our proposal, is the exponentially large number of possible stimuli, which renders the ideal Bayesian solution to the problem computationally intractable. In contrast, the strategy that we propose suggests a realistic implementation in the visual cortex. The implementation involves two populations of cells, one that tracks the position of the image and another that represents a stabilized estimate of the image itself. Spikes from the retina are dynamically routed to the two populations and are interpreted in a probabilistic manner. We consider the architecture of neural circuitry that could implement this strategy and its performance under measured statistics of human fixational eye motion. A salient prediction is that in high acuity tasks, fixed features within the visual scene are beneficial because they provide information about the drifting position of the image. Therefore, complete elimination of peripheral features in the visual scene should degrade performance on high acuity tasks involving very small stimuli.computation | fixational eye motion | neural network | retina | cortex O ur brain infers the structure of its surroundings from the signals of sensory neurons. When those signals are noisy, their interpretation becomes ambiguous, and multiple hypotheses about the outside world compete. Here we consider how the brain estimates a 2D image of the visual scene on the basis of the neural signals from optic nerve fibers. Ambiguity in this process derives from two primary sources: noise in the neural circuitry of the retina and random movements of the eye that lead to image jitter on the retina. An ideal Bayesian decoder in the brain would take these sources of ambiguity into account and evaluate the likelihoods of different 2D scenes leading to the spike trains from the retina. However, the full probability distribution of an image with many pixels includes an unfathomably large number of variables. Prior work on Bayesian inference focused on simplified problems in which the subject estimates only a single, typically static sensory variable (1-5). Thus there is considerable uncertainty whether Bayesian inference of full images is practicable at all. We begin by laying out the stochastic constraints on this process.Humans with normal vision can resolve visual features spanning less than an arcminute, or approximately two receptive fields of ganglion cells in the central fovea, where each ganglion cell receives input from a single cone photoreceptor. Indeed, the letters "E" and "F" on the 20/20 line...

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Section: Resultsmentioning

confidence: 99%

“…Human eye movements resemble a random walk with a diffusion coefficient D ≃ 100 arcmin 2 /s (11)(12)(13). In the 40-ms interval considered above, the resulting image drift can cover some 200 different pixels.…”

Section: Resultsmentioning

confidence: 99%

Bayesian model of dynamic image stabilization in the visual system

Burak

Rokni

Meister

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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“…In humans, the power spectrum of isolated periods of ocular drift. and tremor declines as 1// 2 in the frequency range 0-40 Hz (Eizenman et al, 1985). This spectrum is presumably broadened by l1xational saccades and small movements of the head and body that occur during natural viewing.…”

Section: Discussionmentioning

confidence: 99%

Fixational instability and natural image statistics: Implications for early visual representations

Rucci

Casile

2005

Network: Computation in Neural Systems

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Under natural vicv-.Ting-conditions 1 srnall movements of the eye: head, and body prevent the maintenance of a, steady direction of gaze. It is known tlu.tt stinmli tend to fade when they are stabilized on the retina for scveraJ seconds. Hm:vever: it is undcar whether the physiological motion of the retinal image serves a visual purpose during the brief periods of natural visual fixation. Thb r:;tucly exrtmines the in1paet of Hxational instability on the statistics of the visua.1 input to the retina. and on the structure of neural activity in the crtrly visual system. We sho\\' that fixa.tiona.l im;tnbility introduees a cornponcmt in the retinal input signals that) in the presence of natural images, lacks spatiaJ correlations. This c:omponent strongly infl1.wncc;s neuraJ activity in a model of the LGN. It decorrdates cell responses even if the contrast sensitivity functions of simulated cells arc not perfc-;;ctly tuned to counter-balance the power-bsv spectrum of natura.l i1nagcs. A clecorrelation of neural activity at the early stages of the visual system has been proposed to be bendieial for discarding statistica.l redundancies in the input f:lignals. The results of this study suggest that fixa..tional instability rnight contribute to ef:lta.blbhing efficient representations of natural stimuli.

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“…Repeated application of the cross-correlation while incrementing the strip location one B-scan at a time resulted in 216 motion measurements per volume and a motion sampling rate of 606 Hz. While this rate was well below that of the tracker (16 KHz), it was sufficient to assess tracker performance across the range of frequencies where the vast majority of eye movements reside (<10Hz) [36,37]. The rate also covered what we ultimately determined to be the useful range of the tracker.…”

Section: Imaging Mode #2mentioning

confidence: 96%

Adaptive optics optical coherence tomography with dynamic retinal tracking

Kocaoglu

Ferguson

Jonnal

et al. 2014

Biomed. Opt. Express

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Adaptive optics optical coherence tomography (AO-OCT) is a highly sensitive and noninvasive method for three dimensional imaging of the microscopic retina. Like all in vivo retinal imaging techniques, however, it suffers the effects of involuntary eye movements that occur even under normal fixation. In this study we investigated dynamic retinal tracking to measure and correct eye motion at KHz rates for AO-OCT imaging. A customized retina tracking module was integrated into the sample arm of the 2nd-generation Indiana AO-OCT system and images were acquired on three subjects. Analyses were developed based on temporal amplitude and spatial power spectra in conjunction with strip-wise registration to independently measure AO-OCT tracking performance. After optimization of the tracker parameters, the system was found to correct eye movements up to 100 Hz and reduce residual motion to 10 µm root mean square. Between session precision was 33 µm. Performance was limited by trackergenerated noise at high temporal frequencies. References and links1. E. J. Fernández, B. Povazay, B. Hermann, A. Unterhuber, H. Sattmann, P. M. Prieto, R. Leitgeb, P. Ahnelt, P.Artal, and W. Drexler, "Three-dimensional adaptive optics ultrahigh-resolution optical coherence tomography using a liquid crystal spatial light modulator," Vision Res. 45(28), 3432-3444 (2005) S. Schuman, "Retinal nerve fiber layer assessment using optical coherence tomography with active optic nerve head tracking," Invest. Ophthalmol. Vis. Sci. 47(3), 964-967 (2006). 34. Z. Liu, O. P. Kocaoglu, and D. T. Miller, "In-the-plane design of an off-axis ophthalmic adaptive optics system using toroidal mirrors," Biomed.

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Power spectra for ocular drift and tremor

Cited by 106 publications

References 13 publications

Bayesian model of dynamic image stabilization in the visual system

Bayesian model of dynamic image stabilization in the visual system

Fixational instability and natural image statistics: Implications for early visual representations

Adaptive optics optical coherence tomography with dynamic retinal tracking

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