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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 cannot reproduce the positive-negative parity asymmetry in the psychophysical data.In this numerical experiment, we tuned the coefficients of the weighted 4-quadrant model to optimize a fit to the psychophysical data. (A) The tuned model could reproduce the 2-point glider data well. (B) Although the tuned weighted 4-quadrant model could reproduce the signs of the 3-point glider data, it could not reproduce the differential amplitudes of the positive and negative parity responses. This demonstrates that the architecture of the weighted 4-quadrant model is too limited to reproduce the experimental response pattern.DOI:http://dx.doi.org/10.7554/eLife.09123.009
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fig3s2: The weighted 4-quadrant model cannot reproduce the positive-negative parity asymmetry in the psychophysical data.In this numerical experiment, we tuned the coefficients of the weighted 4-quadrant model to optimize a fit to the psychophysical data. (A) The tuned model could reproduce the 2-point glider data well. (B) Although the tuned weighted 4-quadrant model could reproduce the signs of the 3-point glider data, it could not reproduce the differential amplitudes of the positive and negative parity responses. This demonstrates that the architecture of the weighted 4-quadrant model is too limited to reproduce the experimental response pattern.DOI:http://dx.doi.org/10.7554/eLife.09123.009

Mentions: The performance-optimized weighted 4-quadrant model also offered an interesting interpretation of Drosophila's glider response pattern. First note that the model preserved the HRC's response pattern to 2-point glider stimuli (compare left subpanels of Figure 3D and Figure 1G). More interestingly, the model predicted behavioral responses to 3-point glider stimuli that matched the experimentally observed turning directions, and even the response magnitudes were similar between the model and the data (right, Figure 3D). Nevertheless, the model's predictions were imperfect. The primary qualitative discrepancy was that the model failed to predict that positive 3-point glider stimuli would generate smaller turning responses than negative 3-point glider stimuli. The simplest interpretation for this experimental result is that flies might incorporate both 3-point correlations and 4-point correlations into their motion estimation strategy. In particular, since the positive and negative 3-point glider stimuli have inverted 3-point correlations and matched 4-point correlations, third-order and fourth-order correlations would have the same sign for one parity and opposite signs for the other parity. This observation makes it easier to understand the glider predictions of the weighted 4-quadrant model. The optimized model does a good job accounting for the direction and approximate magnitude of the glider responses because it draws heavily on second-order and odd-order correlations, but it fails to predict the 3-point glider magnitude asymmetry because it finds little added utility in higher-order even correlations (Figure 3—figure supplement 1, Appendix 8). This failure stems from architectural limitations in the weighted 4-quadrant model (Figure 3—figure supplement 2), so it is important to consider alternate model classes.


Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

The weighted 4-quadrant model cannot reproduce the positive-negative parity asymmetry in the psychophysical data.In this numerical experiment, we tuned the coefficients of the weighted 4-quadrant model to optimize a fit to the psychophysical data. (A) The tuned model could reproduce the 2-point glider data well. (B) Although the tuned weighted 4-quadrant model could reproduce the signs of the 3-point glider data, it could not reproduce the differential amplitudes of the positive and negative parity responses. This demonstrates that the architecture of the weighted 4-quadrant model is too limited to reproduce the experimental response pattern.DOI:http://dx.doi.org/10.7554/eLife.09123.009
© Copyright Policy
Related In: Results  -  Collection

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

fig3s2: The weighted 4-quadrant model cannot reproduce the positive-negative parity asymmetry in the psychophysical data.In this numerical experiment, we tuned the coefficients of the weighted 4-quadrant model to optimize a fit to the psychophysical data. (A) The tuned model could reproduce the 2-point glider data well. (B) Although the tuned weighted 4-quadrant model could reproduce the signs of the 3-point glider data, it could not reproduce the differential amplitudes of the positive and negative parity responses. This demonstrates that the architecture of the weighted 4-quadrant model is too limited to reproduce the experimental response pattern.DOI:http://dx.doi.org/10.7554/eLife.09123.009
Mentions: The performance-optimized weighted 4-quadrant model also offered an interesting interpretation of Drosophila's glider response pattern. First note that the model preserved the HRC's response pattern to 2-point glider stimuli (compare left subpanels of Figure 3D and Figure 1G). More interestingly, the model predicted behavioral responses to 3-point glider stimuli that matched the experimentally observed turning directions, and even the response magnitudes were similar between the model and the data (right, Figure 3D). Nevertheless, the model's predictions were imperfect. The primary qualitative discrepancy was that the model failed to predict that positive 3-point glider stimuli would generate smaller turning responses than negative 3-point glider stimuli. The simplest interpretation for this experimental result is that flies might incorporate both 3-point correlations and 4-point correlations into their motion estimation strategy. In particular, since the positive and negative 3-point glider stimuli have inverted 3-point correlations and matched 4-point correlations, third-order and fourth-order correlations would have the same sign for one parity and opposite signs for the other parity. This observation makes it easier to understand the glider predictions of the weighted 4-quadrant model. The optimized model does a good job accounting for the direction and approximate magnitude of the glider responses because it draws heavily on second-order and odd-order correlations, but it fails to predict the 3-point glider magnitude asymmetry because it finds little added utility in higher-order even correlations (Figure 3—figure supplement 1, Appendix 8). This failure stems from architectural limitations in the weighted 4-quadrant model (Figure 3—figure supplement 2), so it is important to consider alternate model classes.

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