Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1.
a seminar from the Institute for Adaptive and Neural Computation (ANC)
- Jan 24, 2012, from 11:00 am to 12:00 pm
- IF 4.31/4.33
- Laurent Perrinet, David Fitzpatrick, James A. Bednar. Edge statistics in natural images versus laboratory animal environments: implications for understanding lateral connectivity in V1, URL . In A seminar from the Institute for Adaptive and Neural Computation (ANC), 2012 abstractOriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves more common than other possible arrangements of edges (Geisler et al., Vis Res 41:711-24, 2001). The visual system appears to take advantage of this prior information, and human contour detection and grouping performance is well predicted by such an "association field" (Field et al., Vis Res 33:173-93, 1993). One possible candidate substrate for implementing an association field in mammals is the set of long-range lateral connections between neurons in the primary visual cortex (V1), which could act to facilitate detection of contours matching the association field, and/or inhibit detection of other contours (Choe and Miikkulainen, Biol Cyb 90:75-88, 2004). To fill this role, the lateral connections would need to be orientation specific and aligned along contours, and indeed such an arrangement has been found in tree shrew primary visual cortex (Bosking et al., J Neurosci 17:2112-27, 1997). However, it is not yet known whether these patterns develop as a result of visual experience, or are simply hard-wired to be appropriate for the statistics of natural scenes.To investigate this issue, we examined the properties of the visual environment of laboratory animals, to determine whether the observed connection patterns are more similar to the statistics of the rearing environment or of a natural habitat. Specifically, we analyzed the cooccurence statistics of edge elements in images of natural scenes, and compared them to corresponding statistics for images taken from within the rearing environment of the animals in the Bosking et al. (1997) study. We used a modified version of the algorithm from Geisler et al. (2001), with a more general edge extraction algorithm that uses sparse coding to avoid multiple responses to a single edge. Collinearity and co-circularity results for natural images replicated qualitatively the results from Geisler et al. (2001), confirming that prior information about continuations appeared consistently in natural images. However, we find that the largely man-made environment in which these animals were reared has a significantly higher probability of collinear edge elements. We thus predict that if the lateral connection patterns are due to visual experience, the patterns in wild-raised tree shrews would be very different from those measured by Bosking et al. (1997), with shorter-range correlations and less emphasis on collinear continuations. This prediction can be tested in future experiments on matching groups of animals reared in different environments.W.H. Bosking and Y. Zhang and B. Schofield and D. Fitzpatrick (1997)Orientation selectivity and the arrangement of horizontal connectionsin tree shrew striate cortexJournal of Neuroscience 17:2112-27.E.M. Callaway and L.C. Katz (1990)Emergence and refinement of clustered horizontal connections in cat striate cortex.Journal of Neuroscience 10:1134–53.Y. Choe and R. Miikkulainen (2004)Contour integration and segmentation with self-organized lateral connectionsBiological Cybernetics 90:75-88.D.J. Field, A. Hayes, and R.F. Hess (1993)Contour integration by the human visual system: Evidence for a local"association field",Vision Research 33:173–93.W.S. Geisler, J.S. Perry, B.J. Super, and D.P. Gallogly (2001)Edge co-occurrence in natural images predicts contour grouping performance.Vision Research 41:711-24..
All material (c) L. Perrinet. Please check the copyright notice.
This work was supported by European Union project Number FP7-269921, "BrainScales".