Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly

Egelhaaf, Martin; Reichardt, Werner

doi:10.1007/bf00317982

Cited by 106 publications

(96 citation statements)

References 58 publications

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“…Even the steady-state responses to constant motion depend, apart from velocity, on the textural properties and contrast of the stimulus pattern (for a review see Egelhaaf and Borst 1993b). Moreover, it has been shown theoretically as well as experimentally that the time course of the spatially integrated output of local movement detectors depends not only on pattern velocity but also on acceleration and even higher temporal derivatives (Egelhaaf and Reichardt 1987). This implies that the time course of the spatially integrated detector response reflects the time course of pattern velocity only within a limited dynamic range, i.e.…”

Section: Specificity Of Neuronal Responses Under Naturalistic and Simmentioning

confidence: 99%

“…Rather it also responds strongly to simple approximations of translational optic flow and even monocular motion Egelhaaf 2000, Hausen 1982b) as well as to purely translatory motion in a three-dimensional environment . Moreover, the response amplitude of the HSE cell was found to depend in a nonlinear way on stimulus parameters, such as velocity, contrast and the textural properties of the stimulus patterns and Reichardt 1987, Hausen 1982a. From these findings, we expected the responses of the HSE cell to behaviourally generated optic flow to provide only highly ambiguous information about the animal's self-motion.…”

Section: Introductionmentioning

confidence: 97%

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Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Kern¹,

Lutterklas²,

Petereit³

et al. 2001

Network: Computation in Neural Systems

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The stimuli traditionally used for analysing visual information processing are much simpler than what an animal sees when moving in its natural environment. Therefore, we analysed in a previous study the performance of an identified neuron in the optomotor system of the fly by using as visual stimuli image sequences that were experienced by the animal while walking in a structured environment. These electrophysiological experiments revealed that the fly visual system computes from behaviourally generated optic flow a rather unambiguous representation of the animal's self-motion. In contrast to conclusions based on simple stimuli, the directions of turns are represented by an interneuron, the HSE cell, quite independent of the spatial layout of the environment and its textural properties when the cell is stimulated with behaviourally generated optic flow. This conclusion is substantiated here by further experimental evidence. Moreover, it is shown that the largely unambiguous responses of the HSE cell to behaviourally generated optic flow can be replicated to a large extent by a network model of the fly's visual motion pathway. These results stress the significance of naturalistic stimuli for analysing what is encoded by neuronal circuits under natural operating conditions.

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Section: Specificity Of Neuronal Responses Under Naturalistic and Simmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Kern¹,

Lutterklas²,

Petereit³

et al. 2001

Network: Computation in Neural Systems

Self Cite

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show abstract

“…5e, f). The response of the neuron thus reflects the animal's rotational velocity in a highly non-linear fashion (Egelhaaf and Reichardt 1987;Haag and Borst 1997;Kern et al 2001b;van Hateren et al 2005).…”

Section: Resultsmentioning

confidence: 99%

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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“…This is achieved by the neurons by pooling motion information from large parts of the visual ®eld. There is both experimental and theoretical evidence that the time-course of pattern velocity can be estimated best if the high spatial frequencies of the stimulus pattern are smoothed out in the input of the movement detection system (Egelhaaf and Reichardt 1987;Egelhaaf and Borst 1993a). Hence, there is no computational need for a detection of spatial details and thus for a high spatial acuity in a visual motion pathway which is primarily devoted to estimating self-motion of the animal.…”

Section: Comparison Of the Results With Data Obtained In Earlier Studiesmentioning

confidence: 99%

Arrangement of optical axes and spatial resolution in the compound eye of the female blowfly Calliphora

Petrowitz

Dahmen²,

Egelhaaf

et al. 2000

J Comp Physiol A

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We determined the optical axes of ommatidia in the wild-type female blow¯y Calliphora by inspecting the deep pseudopupil in large parts of the compound eye. The resulting map of optical axes allowed us to evaluate the spatial resolution in di erent parts of the eye in terms of interommatidial angles as well as the density of optical axes, and to estimate the orientation of ommatidial rows along the hexagonal eye lattice. The optical axes are not homogeneously distributed over the eye. In the frontal visual ®eld the spatial resolution is about two times higher than in its lateral part and about three times higher as compared to the eye's dorsal pole region. The orientation of the ommatidial rows along the eye lattice is not the same for di erent regions of the eye but changes in a characteristic way. The inter-individual variability in the orientation of the ommatidial rows is estimated to be smaller than 8°. The characteristic arrangement of the ommatidial lattice is discussed as an adaptation for e cient evaluation of optic¯ow as induced during self-motions of the animal.

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Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly

Cited by 106 publications

References 58 publications

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

Neuronal processing of behaviourally generated optic flow: experiments and model simulations

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

Arrangement of optical axes and spatial resolution in the compound eye of the female blowfly Calliphora

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