Limits...
Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

Bottom Line: Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations.Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs.This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Science, Harvard University, Cambridge, United States.

ABSTRACT
Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila's visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

Show MeSH

Related in: MedlinePlus

Motion transforms spatial correlations into temporal correlations.(A) An example natural image (van Hateren and van der Schaaf, 1998). (B) When a natural image (top face) moves to the right, streaks in space-time (front face) indicate the direction and speed of the motion. Alternatively, motion influences the temporal correlation structure of visual signals (side face). (C) Second-order correlation function between pairs of spatially separated contrast signals (across the natural image ensemble [van Hateren and van der Schaaf, 1998]). (D) For constant velocity motion, the temporal correlation function between a pair of spatially separated points is shifted and stretched relative to the spatial correlation function. We separated the two points by Drosophila's photoreceptor spacing (5.1°). (E) Example third-order spatial correlation function involving two points in space. (F) As with pairwise correlations, higher-order temporal correlations between spatially separated visual signals are shifted and stretched (relative to higher-order spatial correlation functions) in a manner that indicates the speed and direction of motion.DOI:http://dx.doi.org/10.7554/eLife.09123.015
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4663970&req=5

fig6: Motion transforms spatial correlations into temporal correlations.(A) An example natural image (van Hateren and van der Schaaf, 1998). (B) When a natural image (top face) moves to the right, streaks in space-time (front face) indicate the direction and speed of the motion. Alternatively, motion influences the temporal correlation structure of visual signals (side face). (C) Second-order correlation function between pairs of spatially separated contrast signals (across the natural image ensemble [van Hateren and van der Schaaf, 1998]). (D) For constant velocity motion, the temporal correlation function between a pair of spatially separated points is shifted and stretched relative to the spatial correlation function. We separated the two points by Drosophila's photoreceptor spacing (5.1°). (E) Example third-order spatial correlation function involving two points in space. (F) As with pairwise correlations, higher-order temporal correlations between spatially separated visual signals are shifted and stretched (relative to higher-order spatial correlation functions) in a manner that indicates the speed and direction of motion.DOI:http://dx.doi.org/10.7554/eLife.09123.015

Mentions: In the real world, animals encounter visual environments that are intricately structured and far from random (Appendix figure 1A) (Ruderman and Bialek, 1994; van Hateren and van der Schaaf, 1998; Geisler, 2008). When an animal rotates with constant angular velocity through the environment, the spatiotemporal response profile of the photoreceptor array encodes the velocity of self-motion through the slope of oriented streaks in space-time (front face, Appendix figure 1B) (Adelson and Bergen, 1985). Thus, a visual system with a dense array of noiseless photoreceptors could extract the angular velocity of an arbitrary image by computing the ratio of temporal and spatial derivatives (Potters and Bialek, 1994). The statistics of the image ensemble become relevant once multiple interpretations of the sensory world become logically consistent with the photoreceptor data. In particular, the optimal motion estimator depends on the statistics of the image ensemble when photoreceptors have noise (Potters and Bialek, 1994; Fitzgerald et al., 2011), and a nonzero spacing between photoreceptors introduces ambiguity via aliasing (Potters and Bialek, 1994). In these cases, the animal can use prior information regarding the sensory environment and its motion in order to weigh the plausibility of each sensory interpretation.10.7554/eLife.09123.015Appendix figure 1.Motion transforms spatial correlations into temporal correlations.


Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

Motion transforms spatial correlations into temporal correlations.(A) An example natural image (van Hateren and van der Schaaf, 1998). (B) When a natural image (top face) moves to the right, streaks in space-time (front face) indicate the direction and speed of the motion. Alternatively, motion influences the temporal correlation structure of visual signals (side face). (C) Second-order correlation function between pairs of spatially separated contrast signals (across the natural image ensemble [van Hateren and van der Schaaf, 1998]). (D) For constant velocity motion, the temporal correlation function between a pair of spatially separated points is shifted and stretched relative to the spatial correlation function. We separated the two points by Drosophila's photoreceptor spacing (5.1°). (E) Example third-order spatial correlation function involving two points in space. (F) As with pairwise correlations, higher-order temporal correlations between spatially separated visual signals are shifted and stretched (relative to higher-order spatial correlation functions) in a manner that indicates the speed and direction of motion.DOI:http://dx.doi.org/10.7554/eLife.09123.015
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663970&req=5

fig6: Motion transforms spatial correlations into temporal correlations.(A) An example natural image (van Hateren and van der Schaaf, 1998). (B) When a natural image (top face) moves to the right, streaks in space-time (front face) indicate the direction and speed of the motion. Alternatively, motion influences the temporal correlation structure of visual signals (side face). (C) Second-order correlation function between pairs of spatially separated contrast signals (across the natural image ensemble [van Hateren and van der Schaaf, 1998]). (D) For constant velocity motion, the temporal correlation function between a pair of spatially separated points is shifted and stretched relative to the spatial correlation function. We separated the two points by Drosophila's photoreceptor spacing (5.1°). (E) Example third-order spatial correlation function involving two points in space. (F) As with pairwise correlations, higher-order temporal correlations between spatially separated visual signals are shifted and stretched (relative to higher-order spatial correlation functions) in a manner that indicates the speed and direction of motion.DOI:http://dx.doi.org/10.7554/eLife.09123.015
Mentions: In the real world, animals encounter visual environments that are intricately structured and far from random (Appendix figure 1A) (Ruderman and Bialek, 1994; van Hateren and van der Schaaf, 1998; Geisler, 2008). When an animal rotates with constant angular velocity through the environment, the spatiotemporal response profile of the photoreceptor array encodes the velocity of self-motion through the slope of oriented streaks in space-time (front face, Appendix figure 1B) (Adelson and Bergen, 1985). Thus, a visual system with a dense array of noiseless photoreceptors could extract the angular velocity of an arbitrary image by computing the ratio of temporal and spatial derivatives (Potters and Bialek, 1994). The statistics of the image ensemble become relevant once multiple interpretations of the sensory world become logically consistent with the photoreceptor data. In particular, the optimal motion estimator depends on the statistics of the image ensemble when photoreceptors have noise (Potters and Bialek, 1994; Fitzgerald et al., 2011), and a nonzero spacing between photoreceptors introduces ambiguity via aliasing (Potters and Bialek, 1994). In these cases, the animal can use prior information regarding the sensory environment and its motion in order to weigh the plausibility of each sensory interpretation.10.7554/eLife.09123.015Appendix figure 1.Motion transforms spatial correlations into temporal correlations.

Bottom Line: Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations.Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs.This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Science, Harvard University, Cambridge, United States.

ABSTRACT
Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila's visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly's motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

Show MeSH
Related in: MedlinePlus