Neuronal encoding of object and distance information: a model simulation study on naturalistic optic flow processing

Abstract

We developed a model of the input circuitry of the FD1 cell, an identified motion-sensitive interneuron in the blowfly's visual system. The model circuit successfully reproduces the FD1 cell's most conspicuous property: its larger responses to objects than to spatially extended patterns. The model circuit also mimics the time-dependent responses of FD1 to dynamically complex naturalistic stimuli, shaped by the blowfly's saccadic flight and gaze strategy: the FD1 responses are enhanced when, as a consequence of self-motion, a nearby object crosses the receptive field during intersaccadic intervals. Moreover, the model predicts that these object-induced responses are superimposed by pronounced pattern-dependent fluctuations during movements on virtual test flights in a three-dimensional environment with systematic modifications of the environmental patterns. Hence, the FD1 cell is predicted to detect not unambiguously objects defined by the spatial layout of the environment, but to be also sensitive to objects distinguished by textural features. These ambiguous detection abilities suggest an encoding of information about objects—irrespective of the features by which the objects are defined—by a population of cells, with the FD1 cell presumably playing a prominent role in such an ensemble.

Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis

“…The overall performance of these circuits can be lumped together and has been explained for long by a computational model of local motion detection, the correlation-type elementary motion detector (EMD), which also formed the basis of our study (Reichardt, 1961; Borst and Egelhaaf, 1989, 1993; Egelhaaf and Borst, 1993). Based on this model, the time course of the responses of fly motion sensitive neurons can be well described, even for the dynamically complex stimulus conditions that are encountered during free-flight sequences (Lindemann et al, 2005; Hennig et al, 2011; Hennig and Egelhaaf, 2012). …”

“…At best, these cells are able to represent the average nearness and/or the average pattern properties in relatively large parts of the visual field. The time course of their output signals reflects environmental pattern and spatial properties during translatory intersaccadic flight phases (Kern et al, 2005; Karmeier, 2006; Hennig and Egelhaaf, 2012; Liang et al, 2012). It is still a controversial issue whether and for what computational purpose this information is employed in visually guided orientation behavior, such as in collision avoidance (Tammero and Dickinson, 2002; Lindemann et al, 2008; Kern et al, 2012; Lindemann and Egelhaaf, 2013).…”

“…In accordance with this view, motion sensitive neurons in the visual system of flies as well as of zebra finches were found to represent information about the spatial structure of the environment when stimulated with the retinal image flow that had previously been experienced by free-flying animals. These motion sensitive neurons as well as experimentally validated models of them were found to respond stronger to nearby environmental structures than to more distant ones because the former induced larger retinal velocities (Boeddeker et al, 2005; Kern et al, 2005, 2006; Lindemann et al, 2005; Karmeier et al, 2006; Hennig and Egelhaaf, 2012; Liang et al, 2012; Eckmeier et al, 2013). However, the responses of these motion sensitive neurons are not only affected by retinal velocity, but also reflect the textural properties of moving stimuli, such as their contrast and spatial frequency content (Egelhaaf and Borst, 1993; Straw et al, 2008; Meyer et al, 2011; Hennig and Egelhaaf, 2012).…”

“…These motion sensitive neurons as well as experimentally validated models of them were found to respond stronger to nearby environmental structures than to more distant ones because the former induced larger retinal velocities (Boeddeker et al, 2005; Kern et al, 2005, 2006; Lindemann et al, 2005; Karmeier et al, 2006; Hennig and Egelhaaf, 2012; Liang et al, 2012; Eckmeier et al, 2013). However, the responses of these motion sensitive neurons are not only affected by retinal velocity, but also reflect the textural properties of moving stimuli, such as their contrast and spatial frequency content (Egelhaaf and Borst, 1993; Straw et al, 2008; Meyer et al, 2011; Hennig and Egelhaaf, 2012). This characteristic feature has often irritated researchers because from the perspective of velocity coding the pattern-dependent response modulations may just reflect a kind of “pattern noise” that deteriorates the quality of the neural representation of pattern velocity (Dror et al, 2001; Rajesh et al, 2005; O'Carroll et al, 2011).…”

Visual motion-sensitive neurons in the bumblebee brain convey information about landmarks during a navigational task

“…During the intersaccadic phases of translatory motion bees can gather depth information from the environment as has been shown before for blowflies (Boeddeker et al, 2005; Kern et al, 2005, 2006; Karmeier et al, 2006; Egelhaaf et al, 2012; Hennig and Egelhaaf, 2012; Liang et al, 2012). However, in these analyses of blowflies only spontaneous flights could be considered and objects were introduced virtually in most of these studies after the behavioral experiments had been performed, just for stimulus generation.…”