Accuracy of velocity estimation by Reichardt correlators

Dror, Ron O.; O’Carroll, David C.; Laughlin, Simon B.

doi:10.1364/josaa.18.000241

Cited by 133 publications

(181 citation statements)

References 39 publications

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“…The spatial power spectra of luminance along one-dimensional paths in natural images follow an approximate 1/f characteristic, where f is spatial frequency (Dror et al, 2001;van Hateren, 1997). In addition to similarity between different scenes, this characteristic implies a certain self-similarity of natural imagery at different spatial scales (i.e., at different ranges from the eye), and it leads to a corresponding consistency in the response of the correlational EMD model: velocity tuning curves (mean output versus velocity of optic flow) obtained with different moving natural images tend to be very similar in shape (Dror et al, 2001; see also Fig. 7 below).…”

Section: Velocity Constancymentioning

confidence: 99%

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Velocity constancy and models for wide-field visual motion detection in insects

2005

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Abstract. The tangential neurons in the lobula plate region of the flies are known to respond to visual motion across broad receptive fields in visual space. When intracellular recordings are made from t angential neurons while the intact animal is stimulated visually with moving natural imagery, we find that neural response depends upon speed of motion but is nearly invariant with respect to variations in natural scenery. We refer to this invariance as velocity constancy. It is remarkable because natural scenes, in spite of similarities in spatial structure, vary considerably in contrast, and contrast dependence is a feature of neurons in the early visual pathway as well as of most models for the elementary operations of visual motion detection. Thus, we expect that operations must be present in the processing pathway that reduce contrast dependence in order to approximate velocity constancy. We consider models for such operations, including spatial filtering, motion adaptation, saturating nonlinearities, and nonlinear spatial integration by the tangential neurons themselves, and evaluate their effects in simulations of a tangential neuron and precursor processing in response to animated natural imagery. We conclude that all such features reduce interscene variance in response, but that the model system does not approach velocity constancy as closely as the biological tangential cell.

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Section: Velocity Constancymentioning

confidence: 99%

“…Saturating nonlinearities have been included as elaborations in the correlational model for motion detection, and have been shown to improve reliability of velocity estimation (Dror et al, 2001).…”

Section: Velocity Constancymentioning

confidence: 99%

Velocity constancy and models for wide-field visual motion detection in insects

2005

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“…This is because: (1) natural stimuli are characterized by a wide range of spatial frequencies, in contrast to conventional grating patterns (see also Ref. [60]); (2) the local-movement inputs of the TCs operate in a range where velocity is no longer represented linearly; and (3) the nonlinear spatialintegration characteristics of TCs (see Box 1) make their responses largely independent of texture density.…”

Section: Evaluation Of Behaviourally Generated Optic Flowmentioning

confidence: 99%

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

Egelhaaf

Kern

Krapp

et al. 2002

Trends in Neurosciences

150

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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“…It is well known that the responses of the correlation type motion detector and of the postsynaptic neurons integrating their responses strongly depend on the textural properties of a moving pattern (Buchner 1984;Dror et al 2001;Eckert and Hamdorf 1981). To test whether the wall avoidance behaviour of the saccadic controller is robust against changes in texture, we tested the system for different wall textures.…”

Section: Dependence On the Wall Texturementioning

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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Accuracy of velocity estimation by Reichardt correlators

Cited by 133 publications

References 39 publications

Velocity constancy and models for wide-field visual motion detection in insects

Velocity constancy and models for wide-field visual motion detection in insects

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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