Efficient coding of natural time varying images in the early visual system

Eckert, Michael P.; Buchsbaum, Gershon

doi:10.1098/rstb.1993.0038

Cited by 37 publications

(5 citation statements)

References 45 publications

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“…This provides a functional explanation for previous behavioral studies indicating that speed sensitivity depends on eccentricity [26–28]. This systematic variation in speed tuning across the visual field may be an adaptation to the non-uniform distribution of speed signals induced by selective foveation in natural scenes [32]. From the perspective of decoding, this result suggests that we might further optimize reconstruction by including eccentricity-dependent speed tuning in the prior.…”

Section: Discussionsupporting

confidence: 57%

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

et al. 2011

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Summary Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2], and can form the basis for brain decoding devices [3–5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns, and have shown that it is possible to reconstruct these images from brain activity measurements [6–8]. However, blood oxygen level dependent (BOLD) signals measured using fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcome this limitation. Our motion-energy model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipito-temporal visual cortex of human subjects who passively watched natural movies, and fit the encoding model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent moving stimuli. To demonstrate the power of our approach we also constructed a Bayesian decoder [8], by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of natural movies, capturing the spatio-temporal structure of the viewed movie. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.

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Section: Discussionsupporting

confidence: 57%

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

et al. 2011

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“…DB2, with its lower input rate and fewer outputs, is likely to release quanta tonically and transmit lower temporal frequencies (Freed, 2000b). Transmission of lowfrequency components might be important for foveal parasol cells because eye moments that steady the fovea on a target reduce the range of temporal frequencies in the fovea (Eckert and Buchsbaum, 1993).…”

Section: Discussionmentioning

confidence: 99%

Microcircuitry for Two Types of Achromatic Ganglion Cell in Primate Fovea

Calkins

Sterling²

2007

J. Neurosci.

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Synaptic circuits in primate fovea have been quantified for midget/parvocellular ganglion cells. Here, based on partial reconstructions from serial electron micrographs, we quantify synaptic circuits for two other types of ganglion cell: the familiar parasol/magnocellular cell and a smaller type, termed "garland." The excitatory circuits both derive from two types of OFF diffuse cone bipolar cell, DB3 and DB2, which collected unselectively from at least 6 Ϯ 1 cones, including the S type. Cone contacts to DB3 dendrites were usually located between neighboring triads, whereas half of the cone contacts to DB2 were triad associated. Ribbon outputs were as follows: DB3, 69 Ϯ 5; DB2, 48 Ϯ 4. A complete parasol cell (30 m dendritic field diameter) would collect from ϳ50 cones via ϳ120 bipolar and ϳ85 amacrine contacts; a complete garland cell (25 m dendritic field) would collect from ϳ40 cones via ϳ75 bipolar and ϳ145 amacrine contacts. The bipolar types contributed differently: the parasol cell received most contacts (60%) from DB3, whereas the garland cell received most contacts (67%) from DB2. We hypothesize that DB3 is a transient bipolar cell and that DB2 is sustained. This would be consistent with their relative inputs to the brisk-transient (parasol) ganglion cell. The garland cell, with its high proportion of DB2 inputs plus its high proportion of amacrine synapses (70%) and dense mosaic, might correspond to the local-edge cell in nonprimate retinas, which serves finer acuity at low temporal frequencies. The convergence of S cones onto both types could contribute S-cone input for cortical areas primary visual cortex and the middle temporal area.

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“…The statistical properties of a wide variety of natural scenes have been characterized in many studies. The scenes analysed were usually stationary, or they resulted from movements either at constant velocities or with dynamics that differ a lot from that of unrestrained gaze changes during natural locomotion (e.g., Eckert and Buchsbaum, 1993; Dong and Attick, 1995; van Hateren, 1997; Simoncelli and Olshausen, 2001; Betsch et al, 2004; Geisler, 2008). In a recent study, we simulated the natural dynamics of the saccadic gaze strategy of insects and registered the resulting image sequences in a large variety of natural environments (Schwegmann et al, in preparation).…”

Section: Processing Of Optic Flow In the Insect Nervous Systemmentioning

confidence: 99%

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Egelhaaf¹,

Boeddeker²,

Kern³

et al. 2012

Front. Neural Circuits

126

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Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes (“optic flow”). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action–perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor.

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Efficient coding of natural time varying images in the early visual system

Cited by 37 publications

References 45 publications

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

Microcircuitry for Two Types of Achromatic Ganglion Cell in Primate Fovea

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

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