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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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Front-end nonlinearities modify the correlations present in natural scenes.(A) Example images with no front-end nonlinearity (top), with an equalizing front-end nonlinearity (middle), and with a binarizing front-end nonlinearity (bottom). (B) The covariance between contrasts at 2 horizontally separated points is plotted as a function of distance between the points. The binary nonlinearity attenuated spatial correlations.DOI:http://dx.doi.org/10.7554/eLife.09123.006
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fig2s1: Front-end nonlinearities modify the correlations present in natural scenes.(A) Example images with no front-end nonlinearity (top), with an equalizing front-end nonlinearity (middle), and with a binarizing front-end nonlinearity (bottom). (B) The covariance between contrasts at 2 horizontally separated points is plotted as a function of distance between the points. The binary nonlinearity attenuated spatial correlations.DOI:http://dx.doi.org/10.7554/eLife.09123.006

Mentions: The Gaussian, uniform, and symmetric Bernoulli distributions have much lower kurtosis values (kurtosis = 3.0, 1.8, 1.0, respectively, Figure 2D). In fact, the symmetric Bernoulli distribution has the lowest kurtosis of any probability distribution (DeCarlo, 1997). When we transformed the HRC's inputs to have these statistics (‘Materials and methods’), we found that each nonlinearity substantially improved the accuracy of the HRC (Figure 2E). The contrast equalizing nonlinearity, which produces uniform outputs, performed best and also plays a prominent role in efficient coding theory (Laughlin, 1981). It is interesting that contrast equalization improved the accuracy of the HRC more than binarization (Figure 2E), even though it produced outputs with greater kurtosis. The reason for this is that natural images are spatially correlated, and the accuracy of the HRC over a general image ensemble depends on the ensemble's spatial correlation structure (Appendix 2). Binarization attenuated spatial correlations more strongly than contrast equalization over the natural image ensemble (Figure 2—figure supplement 1), and spatial correlations can enhance the performance of the HRC (Appendices 4, 5). Designing a nonlinearity that optimally sculpts the correlation structure of natural images is not simple and goes beyond the scope of this study.


Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

Front-end nonlinearities modify the correlations present in natural scenes.(A) Example images with no front-end nonlinearity (top), with an equalizing front-end nonlinearity (middle), and with a binarizing front-end nonlinearity (bottom). (B) The covariance between contrasts at 2 horizontally separated points is plotted as a function of distance between the points. The binary nonlinearity attenuated spatial correlations.DOI:http://dx.doi.org/10.7554/eLife.09123.006
© Copyright Policy
Related In: Results  -  Collection

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

fig2s1: Front-end nonlinearities modify the correlations present in natural scenes.(A) Example images with no front-end nonlinearity (top), with an equalizing front-end nonlinearity (middle), and with a binarizing front-end nonlinearity (bottom). (B) The covariance between contrasts at 2 horizontally separated points is plotted as a function of distance between the points. The binary nonlinearity attenuated spatial correlations.DOI:http://dx.doi.org/10.7554/eLife.09123.006
Mentions: The Gaussian, uniform, and symmetric Bernoulli distributions have much lower kurtosis values (kurtosis = 3.0, 1.8, 1.0, respectively, Figure 2D). In fact, the symmetric Bernoulli distribution has the lowest kurtosis of any probability distribution (DeCarlo, 1997). When we transformed the HRC's inputs to have these statistics (‘Materials and methods’), we found that each nonlinearity substantially improved the accuracy of the HRC (Figure 2E). The contrast equalizing nonlinearity, which produces uniform outputs, performed best and also plays a prominent role in efficient coding theory (Laughlin, 1981). It is interesting that contrast equalization improved the accuracy of the HRC more than binarization (Figure 2E), even though it produced outputs with greater kurtosis. The reason for this is that natural images are spatially correlated, and the accuracy of the HRC over a general image ensemble depends on the ensemble's spatial correlation structure (Appendix 2). Binarization attenuated spatial correlations more strongly than contrast equalization over the natural image ensemble (Figure 2—figure supplement 1), and spatial correlations can enhance the performance of the HRC (Appendices 4, 5). Designing a nonlinearity that optimally sculpts the correlation structure of natural images is not simple and goes beyond the scope of this study.

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