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Borst, Alexander

doi:10.1023/a:1021172200868

Cited by 26 publications

(12 citation statements)

References 13 publications

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“…On the basis of individual response traces, it is not easily possible to discern stimulus-driven activity changes from those that are due to sources not associated with the stimulus (“noise”). The origin of various potential noise sources in the visual motion pathway and the consequences of the unreliable nature of neural signals have been analysed in flies (e.g., de Ruyter van Steveninck and Bialek, 1995; de Ruyter van Steveninck et al, 1997; Warzecha and Egelhaaf, 1999; Warzecha et al, 2000; Egelhaaf et al, 2001; Lewen et al, 2001; Borst, 2003; Grewe et al, 2003, 2007; Nemenman et al, 2008). These aspects, as well as the impact of neuronal noise on the precision with which motion information can be encoded, have been controversially discussed (Haag and Borst, 1997, 1998; Warzecha and Egelhaaf, 1997; Warzecha et al, 1998, 2000, 2003; Brenner et al, 2000b; Fairhall et al, 2001; Kalb, 2006).…”

Section: Behavioral Significance Of Optic Flow Neuronsmentioning

confidence: 99%

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Egelhaaf¹,

Boeddeker²,

Kern³

et al. 2012

Front. Neural Circuits

126

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Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes (“optic flow”). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action–perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor.

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Section: Behavioral Significance Of Optic Flow Neuronsmentioning

confidence: 99%

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Egelhaaf¹,

Boeddeker²,

Kern³

et al. 2012

Front. Neural Circuits

126

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“…Moreover, at low light intensities, the combination of the discrete nature of light and the high sensitivity of the photoreceptors introduces noise into the system, which seems to be responsible for around 50% of the noise measured in the response of postsynaptic cells [12]. The consequences of this noise further downstream the visual pathway are still being debated [13]–[17].…”

Section: Introductionmentioning

confidence: 99%

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

2010

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To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervous system changes with temperature and luminance. Although these two environmental variables have a considerable impact on the fly's nervous system, they do not impede the fly to behave suitably over a wide range of conditions. We recorded responses from a motion-sensitive neuron, the H1-cell, to a time-varying stimulus at many different combinations of temperature and luminance. We found that the mean firing rate, but not firing precision, changes with temperature, while both were affected by mean luminance. Because we also found that information rate and coding efficiency are mainly set by the mean firing rate, our results suggest that, in the face of environmental perturbations, the coding efficiency is improved by an increase in the mean firing rate, rather than by an increased firing precision.

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“…The first experiments addressing this point were performed, again on the fly visual system using the motion-sensitive neuron H1, by Brenner et al (2000), later confirmed and extended by Fairhall et al (2001) and Borst (2003). In all the three studies, flies were stimulated with a rigidly moving spatial pattern.…”

Section: Resultsmentioning

confidence: 90%

“…Here, instead of the method reported in Brenner et al (2000), Fairhall et al 2001 andBorst (2003), the immediate responses of both the model and the fly neuron are displayed. In order to have comparable output signals, the model response was fed into a leaky integrate-and-fire neuron.…”

Section: Resultsmentioning

confidence: 99%

“…Note that in this simulation, in order to compare the model and experimental data in a oneto-one fashion and to use identical software to evaluate responses from both sources, the output of the Reichardt detector array was used to drive a leaky integrate-and-fire neuron, resulting in a spike output of the model too. Information was calculated from the spike output according to the method as outlined in de Ruyter et al (1997) and Borst (2003). actual velocity input, it is most unfavourable when the noisiness of the visual input is considered (figure 2).…”

Section: Correlation Versus Gradient Detectors a Borst 371mentioning

confidence: 99%

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Correlation versus gradient type motion detectors: the pros and cons

Borst¹

2010

Modelling Perception With Artificial Neural Networks

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Visual motion contains a wealth of information about self-motion as well as the three-dimensional structure of the environment. Therefore, it is of utmost importance for any organism with eyes. However, visual motion information is not explicitly represented at the photoreceptor level, but rather has to be computed by the nervous system from the changing retinal images as one of the first processing steps. Two prominent models have been proposed to account for this neural computation: the Reichardt detector and the gradient detector. While the Reichardt detector correlates the luminance levels derived from two adjacent image points, the gradient detector provides an estimate of the local retinal image velocity by dividing the spatial and the temporal luminance gradient. As a consequence of their different internal processing structure, both the models differ in a number of functional aspects such as their dependence on the spatial-pattern structure as well as their sensitivity to photon noise. These different properties lead to the proposal that an ideal motion detector should be of Reichardt type at low luminance levels, but of gradient type at high luminance levels. However, experiments on the fly visual systems provided unambiguous evidence in favour of the Reichardt detector under all luminance conditions. Does this mean that the fly nervous system uses suboptimal computations, or is there a functional aspect missing in the optimality criterion? In the following, I will argue in favour of the latter, showing that Reichardt detectors have an automatic gain control allowing them to dynamically adjust their input-output relationships to the statistical range of velocities presented, while gradient detectors do not have this property. As a consequence, Reichardt detectors, but not gradient detectors, always provide a maximum amount of information about stimulus velocity over a large range of velocities. This important property might explain why Reichardt type of computations have been demonstrated to underlie the extraction of motion information in the fly visual system under all luminance levels.

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Untitled

Cited by 26 publications

References 13 publications

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Coding Efficiency of Fly Motion Processing Is Set by Firing Rate, Not Firing Precision

Correlation versus gradient type motion detectors: the pros and cons

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