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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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Structure of non-multiplicative nonlinearities in the extra input model of Figure 5B.(A) The nearest-neighbor non-multiplicative nonlinearity was made up of a standard HRC and a 3-point correlator in which the low-pass filtered input was squared before being multiplied by the adjacent receptor's high-pass filtered signal. (B) The next-nearest-neighbor non-multiplicative nonlinearity combined the analogous long-range terms. (C) Structure of the 2-dimensional nonlinearities, shown according to the same conventions as Figure 4B.DOI:http://dx.doi.org/10.7554/eLife.09123.014
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fig5s1: Structure of non-multiplicative nonlinearities in the extra input model of Figure 5B.(A) The nearest-neighbor non-multiplicative nonlinearity was made up of a standard HRC and a 3-point correlator in which the low-pass filtered input was squared before being multiplied by the adjacent receptor's high-pass filtered signal. (B) The next-nearest-neighbor non-multiplicative nonlinearity combined the analogous long-range terms. (C) Structure of the 2-dimensional nonlinearities, shown according to the same conventions as Figure 4B.DOI:http://dx.doi.org/10.7554/eLife.09123.014

Mentions: The leading 16 predictors compactly illustrated how the extra input nonlinearity model recapitulates the conceptual advances offered by the other models (Figure 5B). Four of the predictors combine to implement a mirror-symmetric non-multiplicative nonlinearity model that acts on the first and second points in space (first term, Figure 5B). The dominant contribution to the nonlinearity is the HRC's multiplier, but an additional third-order term breaks the symmetry between positive and negative low-pass filtered signals (Figure 5—figure supplement 1). Thus, the extra input nonlinearity model approximately correlates neighboring points in space, as the HRC would suggest, but it differentially weights positive and negative low-pass filtered signals, like the weighted 4-quadrant model. It also replicates the main insight from the non-multiplicative nonlinearity model: the best treatment of asymmetric light and dark information need not be as simple as pure ON/OFF segregation. The model used another eight predictors to construct two more non-multiplicative nonlinearity models, one that surveyed the second and third points in space and another that surveyed the first and third points (first and second terms, Figure 5B, Figure 5—figure supplement 1). These components make the previously highlighted conceptual points and add the observation that spatial averaging improves estimates.


Nonlinear circuits for naturalistic visual motion estimation.

Fitzgerald JE, Clark DA - Elife (2015)

Structure of non-multiplicative nonlinearities in the extra input model of Figure 5B.(A) The nearest-neighbor non-multiplicative nonlinearity was made up of a standard HRC and a 3-point correlator in which the low-pass filtered input was squared before being multiplied by the adjacent receptor's high-pass filtered signal. (B) The next-nearest-neighbor non-multiplicative nonlinearity combined the analogous long-range terms. (C) Structure of the 2-dimensional nonlinearities, shown according to the same conventions as Figure 4B.DOI:http://dx.doi.org/10.7554/eLife.09123.014
© Copyright Policy
Related In: Results  -  Collection

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

fig5s1: Structure of non-multiplicative nonlinearities in the extra input model of Figure 5B.(A) The nearest-neighbor non-multiplicative nonlinearity was made up of a standard HRC and a 3-point correlator in which the low-pass filtered input was squared before being multiplied by the adjacent receptor's high-pass filtered signal. (B) The next-nearest-neighbor non-multiplicative nonlinearity combined the analogous long-range terms. (C) Structure of the 2-dimensional nonlinearities, shown according to the same conventions as Figure 4B.DOI:http://dx.doi.org/10.7554/eLife.09123.014
Mentions: The leading 16 predictors compactly illustrated how the extra input nonlinearity model recapitulates the conceptual advances offered by the other models (Figure 5B). Four of the predictors combine to implement a mirror-symmetric non-multiplicative nonlinearity model that acts on the first and second points in space (first term, Figure 5B). The dominant contribution to the nonlinearity is the HRC's multiplier, but an additional third-order term breaks the symmetry between positive and negative low-pass filtered signals (Figure 5—figure supplement 1). Thus, the extra input nonlinearity model approximately correlates neighboring points in space, as the HRC would suggest, but it differentially weights positive and negative low-pass filtered signals, like the weighted 4-quadrant model. It also replicates the main insight from the non-multiplicative nonlinearity model: the best treatment of asymmetric light and dark information need not be as simple as pure ON/OFF segregation. The model used another eight predictors to construct two more non-multiplicative nonlinearity models, one that surveyed the second and third points in space and another that surveyed the first and third points (first and second terms, Figure 5B, Figure 5—figure supplement 1). These components make the previously highlighted conceptual points and add the observation that spatial averaging improves estimates.

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