Nonlinear circuits for naturalistic visual motion estimation.
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
Show MeSH
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. |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC4663970&req=5
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). |
View Article: PubMed Central - PubMed
Affiliation: Center for Brain Science, Harvard University, Cambridge, United States.