Binocular Contributions to Optic Flow Processing in the Fly Visual System

Krapp, Holger G.; Hengstenberg, R; Egelhaaf, Martin

doi:10.1152/jn.2001.85.2.724

Cited by 147 publications

(205 citation statements)

References 41 publications

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“…This evidence indicates that, in contrast to previous conclusions (e.g. Haag and Borst 2001;Hausen 1982a, b;Horstmann et al 2000;Kern et al 2001b;Krapp et al 2001), the responses of HScells to translational optic flow may play an important role in orientation behaviour.…”

Section: Introductioncontrasting

confidence: 91%

“…For the left HSE the mirrored input field covers À120°£ / £ 50°in azimuth 0°corresponds to the frontal equatorial direction. The weights of the different movement detectors throughout the visual field are tuned according to the known spatial sensitivity distribution of the HSECell (after Hausen 1982a, b;Krapp et al 2001). A firstorder low-pass filter (time constant 8 ms) applied to the integrated signal approximates the temporal filtering properties of the neuron.…”

Section: Modelling Motion Sensitive Neuronsmentioning

confidence: 99%

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Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

et al. 2005

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The retinal image flow a blowfly experiences in its daily life on the wing is determined by both the structure of the environment and the animal's own movements. To understand the design of visual processing mechanisms, there is thus a need to analyse the performance of neurons under natural operating conditions. To this end, we recorded flight paths of flies outdoors and reconstructed what they had seen, by moving a panoramic camera along exactly the same paths. The reconstructed image sequences were later replayed on a fast, panoramic flight simulator to identified, motion sensitive neurons of the so-called horizontal system (HS) in the lobula plate of the blowfly, which are assumed to extract self-motion parameters from optic flow. We show that under real life conditions HS-cells not only encode information about self-rotation, but are also sensitive to translational optic flow and, thus, indirectly signal information about the depth structure of the environment. These properties do not require an elaboration of the known model of these neurons, because the natural optic flow sequences generate-at least qualitatively-the same depth-related response properties when used as input to a computational HS-cell model and to real neurons.

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Section: Introductioncontrasting

confidence: 91%

Section: Modelling Motion Sensitive Neuronsmentioning

confidence: 99%

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

et al. 2005

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“…The preferred directions of the small-field elements that synapse onto a given TC appear to coincide with the directions of the velocity vectors characterizing the optic flow induced during particular types of self-motion (Fig. 1a) [27, 29,30]. The sophisticated global patterns of preferred directions do not depend on visual experience and thus represent phylogenetic rather than developmental adaptations to optic-flow analysis [31].…”

Section: Exploiting Global Features Of Optic Flow By Spatial Pooling mentioning

confidence: 99%

“…By combining motion information from both eyes (Box 1) specificity may be greatly enhanced. This is accomplished by TCs that convey motion information gathered within the visual field of one eye to the contralateral side of the brain where they interact with other TCs [12,13,30,[37][38][39]. Unless the intervening synapse is carefully adjusted to the presynaptic activity levels that occur during sensory stimulation, synaptic transmission may distort the information being transmitted.…”

Section: Combining Information On Optic Flow From Both Eyesmentioning

confidence: 99%

Neural encoding of behaviourally relevant visual-motion information in the fly

Egelhaaf

Kern

Krapp

et al. 2002

Trends in Neurosciences

149

109

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The goal of neuroethology is to explain behaviour in terms of the activity of nerve cells and their interactions. This can only be achieved if the experimental animal can be analysed at different levels ranging from behaviour to individual neurons. Cellular mechanisms underlying processing of neuronal information are frequently analysed using in vitro preparations where artificial stimulation replaces the natural sensory input. Although such studies provide fascinating insights into the complex computational abilities of neurons [1], the results may not be extrapolated easily to in vivo conditions, where the range of response amplitudes of neurons and their temporal activity patterns may differ considerably from artificially induced activity. In systems such as the retina of the horseshoe crab [5,6], it is now feasible to analyse the neuronal representation of visual input as it is experienced during behaviour (reviewed in Refs [7,8]). Until now, however, in most systems the underlying neuronal mechanisms have been difficult to unravel.In the fly it is possible to employ both quantitative behavioural approaches as well as in vivo electrophysiological and imaging methods to analyse how behaviourally relevant visual input is processed [9][10][11][12][13][14][15][16][17][18][19][20]. Although the latter techniques are mainly employed in the blowfly, which is relatively big, they are complemented by studies of the smaller fruitfly, Drosophila, where a broad range of genetic approaches can be applied to dissect the visual system in an increasingly specific way [21,22].We review recent progress on the encoding of optic-flow information in the blowfly. Optic flow is an important source of information about self-motion and the three-dimensional layout of the environment, not only for flies but for most moving animals including humans (Box 1, [4,[23][24][25]). Flies exploit optic flow to guide their locomotion [13] and to control compensatory head movements [26], and understanding the computational principles underlying optic-flow processing in flies could provide insights into visual-motion analysis in general.Information processing in visual systems is constrained by the spatial and temporal characteristics of the sensory input and by the biophysical properties of the neuronal circuits. Hence, to understand how visual systems encode behaviourally relevant information, we need to know about both the computational capabilities of the nervous system and the natural conditions under which animals normally operate. By combining behavioural, neurophysiological and computational approaches, it is now possible in the fly to assess adaptations that process visual-motion information under the constraints of its natural input. It is concluded that neuronal operating ranges and coding strategies appear to be closely matched to the inputs the animal encounters under behaviourally relevant conditions.

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“…In each subunit, the low-pass filtered signal of one input channel is multiplied with the high-pass filtered signal of the neighbouring input channel. Before the outputs of the movement detector subunits are spatially pooled by the model HSE-cell, their signals are differently weighted, according to the spatial sensitivity distribution of the HSE-cell (Krapp et al 2001). Maximal sensitivity of the simulated neuron is at 15 • lateral to the body long axis (see Lindemann et al 2005 for details).…”

Section: Sensory Modelmentioning

confidence: 99%

Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly

et al. 2008

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Behavioural and electrophysiological experiments suggest that blowflies employ an active saccadic strategy of flight and gaze control to separate the rotational from the translational optic flow components. As a consequence, this allows motion sensitive neurons to encode during translatory intersaccadic phases of locomotion information about the spatial layout of the environment. So far, it has not been clear whether and how a motor controller could decode the responses of these neurons to prevent a blowfly from colliding with obstacles. Here we propose a simple model of the blowfly visual course control system, named cyberfly, and investigate its performance and limitations. The sensory input module of the cyberfly emulates a pair of output neurons subserving the two eyes of the blowfly visual motion pathway. We analyse two sensory-motor interfaces (SMI). An SMI coupling the differential signal of the sensory neurons proportionally to the yaw rotation fails to avoid obstacles. A more plausible SMI is based on a saccadic controller. Even with sideward drift after saccades as is characteristic of real blowflies, the cyberfly is able to successfully avoid collisions with obstacles. The relative distance information contained in the optic flow during translatory movements between saccades is provided to the SMI by the responses of the visual output neurons. An obvious limitation of this simple mechanism is its strong dependence on the textural properties of the environment.

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Binocular Contributions to Optic Flow Processing in the Fly Visual System

Cited by 147 publications

References 41 publications

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

Neural encoding of behaviourally relevant visual-motion information in the fly

Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly

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