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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.

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The weighted 4-quadrant model in the basis of principal components (PCs).(A) We computed the covariance matrix of quadrant responses across the simulated ensemble of naturalistic motions. (B) The eigenvectors of the covariance matrix are called PCs. Signals from the (+ +) and (+ −) quadrants primarily comprised the first two PCs, whereas the (− +) and (− −) components comprised the third and fourth PCs. (C) The first two PCs accounted for the vast majority of the weighted 4-quadrant model's response variance. (D) Each member of the ensemble of naturalistic motions comprised a velocity and a natural image, and both components contributed variance to the model response. Although the first two PCs accounted for most of the variance, little of that variance was associated with the velocity of motion. Instead, the third and fourth PCs best aided motion estimation, because they contributed the vast majority of the velocity-associated variance.DOI:http://dx.doi.org/10.7554/eLife.09123.018
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fig9: The weighted 4-quadrant model in the basis of principal components (PCs).(A) We computed the covariance matrix of quadrant responses across the simulated ensemble of naturalistic motions. (B) The eigenvectors of the covariance matrix are called PCs. Signals from the (+ +) and (+ −) quadrants primarily comprised the first two PCs, whereas the (− +) and (− −) components comprised the third and fourth PCs. (C) The first two PCs accounted for the vast majority of the weighted 4-quadrant model's response variance. (D) Each member of the ensemble of naturalistic motions comprised a velocity and a natural image, and both components contributed variance to the model response. Although the first two PCs accounted for most of the variance, little of that variance was associated with the velocity of motion. Instead, the third and fourth PCs best aided motion estimation, because they contributed the vast majority of the velocity-associated variance.DOI:http://dx.doi.org/10.7554/eLife.09123.018

Mentions: We began by directly applying PCA to the weighted 4-quadrant model. We computed the 4 × 4 covariance matrix of the four quadrants over the ensemble of simulated motions (Appendix figure 4A). The eigenvectors of the covariance matrix are called the PCs (Appendix figure 4B), and the associated eigenvalues specify the amount of variance accounted for by each PC (Appendix figure 4C). We found that the first two PCs accounted for 86.3% of the variance, whereas the third and fourth PCs each contributed about 7% of the variance (Appendix figure 4C). The high-variance eigenvectors roughly corresponded to a sum and a difference of the (+ +) and (+ −) quadrants, whereas the low-variance PCs roughly corresponded to a sum and a difference of the (− +) and (− −) quadrants (Appendix figure 4B). The (− −) and (− +) quadrants best facilitated motion estimation (Figure 3C). Thus, the low-variance PCs were most important for motion estimation.10.7554/eLife.09123.018Appendix figure 4.The weighted 4-quadrant model in the basis of principal components (PCs).


Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

The weighted 4-quadrant model in the basis of principal components (PCs).(A) We computed the covariance matrix of quadrant responses across the simulated ensemble of naturalistic motions. (B) The eigenvectors of the covariance matrix are called PCs. Signals from the (+ +) and (+ −) quadrants primarily comprised the first two PCs, whereas the (− +) and (− −) components comprised the third and fourth PCs. (C) The first two PCs accounted for the vast majority of the weighted 4-quadrant model's response variance. (D) Each member of the ensemble of naturalistic motions comprised a velocity and a natural image, and both components contributed variance to the model response. Although the first two PCs accounted for most of the variance, little of that variance was associated with the velocity of motion. Instead, the third and fourth PCs best aided motion estimation, because they contributed the vast majority of the velocity-associated variance.DOI:http://dx.doi.org/10.7554/eLife.09123.018
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4663970&req=5

fig9: The weighted 4-quadrant model in the basis of principal components (PCs).(A) We computed the covariance matrix of quadrant responses across the simulated ensemble of naturalistic motions. (B) The eigenvectors of the covariance matrix are called PCs. Signals from the (+ +) and (+ −) quadrants primarily comprised the first two PCs, whereas the (− +) and (− −) components comprised the third and fourth PCs. (C) The first two PCs accounted for the vast majority of the weighted 4-quadrant model's response variance. (D) Each member of the ensemble of naturalistic motions comprised a velocity and a natural image, and both components contributed variance to the model response. Although the first two PCs accounted for most of the variance, little of that variance was associated with the velocity of motion. Instead, the third and fourth PCs best aided motion estimation, because they contributed the vast majority of the velocity-associated variance.DOI:http://dx.doi.org/10.7554/eLife.09123.018
Mentions: We began by directly applying PCA to the weighted 4-quadrant model. We computed the 4 × 4 covariance matrix of the four quadrants over the ensemble of simulated motions (Appendix figure 4A). The eigenvectors of the covariance matrix are called the PCs (Appendix figure 4B), and the associated eigenvalues specify the amount of variance accounted for by each PC (Appendix figure 4C). We found that the first two PCs accounted for 86.3% of the variance, whereas the third and fourth PCs each contributed about 7% of the variance (Appendix figure 4C). The high-variance eigenvectors roughly corresponded to a sum and a difference of the (+ +) and (+ −) quadrants, whereas the low-variance PCs roughly corresponded to a sum and a difference of the (− +) and (− −) quadrants (Appendix figure 4B). The (− −) and (− +) quadrants best facilitated motion estimation (Figure 3C). Thus, the low-variance PCs were most important for motion estimation.10.7554/eLife.09123.018Appendix figure 4.The weighted 4-quadrant model in the basis of principal components (PCs).

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