Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila

Abstract

A moving visual figure may contain first-order signals defined by variation in mean luminance, as well as second-order signals defined by constant mean luminance and variation in luminance envelope, or higher-order signals that cannot be estimated by taking higher moments of the luminance distribution. Separating these properties of a moving figure to experimentally probe the visual subsystems that encode them is technically challenging and has resulted in debated mechanisms of visual object detection by flies. Our prior work took a white noise systems identification approach using a commercially available electronic display system to characterize the spatial variation in the temporal dynamics of two distinct subsystems for first- and higher-order components of visual figure tracking. The method relied on the use of single pixel displacements of two visual stimuli according to two binary maximum length shift register sequences (m-sequences) and cross-correlation of each m-sequence with time-varying flight steering measurements. The resultant spatio-temporal action fields represent temporal impulse responses parameterized by the azimuthal location of the visual figure, one STAF for first-order and another for higher-order components of compound stimuli. Here we review m-sequence and reverse correlation procedures, then describe our application in detail, provide Matlab code, validate the STAFs, and demonstrate the utility and robustness of STAFs by predicting the results of other published experimental procedures. This method has demonstrated how two relatively modest innovations on classical white noise analysis—the inclusion of space as a way to organize response kernels and the use of linear decoupling to measure the response to two channels of visual information simultaneously—could substantially improve our basic understanding of visual processing in the fly.

Neurons Forming Optic Glomeruli Compute Figure–Ground Discriminations inDrosophila

“…For a wide bar, fixation is bistable, with two peaks in the fixation histogram corresponding to the position of the bar's edges; the salient component of the figure is its edge rather than center of mass (Wehner and Flatt, 1972;Heisenberg and Wolf, 1984;Maimon et al, 2010;van Breugel and Dickinson, 2012). We extended those studies with a white-noise systems identification approach, the STAF, to analyze figure-tracking behavior (Aptekar et al, 2012). Comparing the STAFs reveals that the EM and FM responses are affected in a qualitatively different manner by figure width (Fig.…”

“…Figure detection operates on a single edge ( Fig. 1C), varies over the visual field (Theobald et al, 2008;Reiser and Dickinson, 2010;Aptekar et al, 2012;Bahl et al, 2013;Fox and Frye, 2014;Fox et al, 2014), and interacts with ground stabilization (Fei et al, 2010;Fox and Frye, 2014). We generated a stimulus set to examine the perceptual similarity of figures defined by one or both edges, and figures on moving grounds (Fig.…”

“…We displayed a narrow figure (F), a proceeding edge (pE), and a receding edge (rE) on a stationary background, and also each on a counter-rotating ground (FG, pEG, and rEG). We included a ground-alone stimulus (G) and a theta figure (tF); an important stimulus for assessing the differential influence of the FM and small-field EM (sfEM) systems to figure-tracking (Zanker, 1996;Theobald et al, 2008;Aptekar et al, 2012). These eight stimuli (G, FG, rEG, rE, F, pE, pEG, tF) in two directions composed our 16 experiments (Fig.…”

“…We reasoned that two dynamic processes are similar if they covary. However, we knew from previous behavioral work that figure and figure-ground stimuli only evoke similar responses within a limited range of the visual field (Aptekar et al, 2012;Fox et al, 2014). This means that to measure the extent to which two responses are similar by covariance, we needed to calculate the covariance between responses over a limited time (space)-window.…”

“…Flies are capable of figure-tracking when the figure stimulus is discriminable from the ground only by its relative motion, even when it occupies Ͻ10% of the visual field, or is superimposed on a ground that moves opposite the direction of the figure, moves randomly, or flickers (Reichardt et al, 1989;Theobald et al, 2010;Fox et al, 2014). Neural mechanisms for the discrimination of vertical edges from the ground image likely evolved for navigating gaps and approaching perch sites within natural landscapes (Maimon et al, 2008;Aptekar et al, 2012).…”