Encoding of Visual Motion Information and Reliability in Spiking and Graded Potential Neurons

Haag, Juergen; Borst, Alexander

doi:10.1523/jneurosci.17-12-04809.1997

Cited by 93 publications

(74 citation statements)

References 39 publications

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“…Because computations that underlie direction selectivity inevitably require time constants of some tens of milliseconds [50], they attenuate the neural responses to high-frequency velocity fluctuations (Fig. 3c) [51,52]. Hence, TC depolarizations are sufficiently pronounced to elicit spikes with a millisecond precision only when the velocity changes are very rapid and large [17,18,53].…”

Section: Accuracy Of Encoding Of Optic Flow Informationmentioning

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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Section: Accuracy Of Encoding Of Optic Flow Informationmentioning

confidence: 99%

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

Egelhaaf

Kern

Krapp

et al. 2002

Trends in Neurosciences

150

109

View full text Add to dashboard Cite

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“…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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“…Moreover, it has been found that the velocity of a randomly fluctuating motion stimulus is represented by spiking and graded potential TCs similarly well -as long as the pattern moves in the cell's preferred direction. Velocity fluctuations above 10-20 Hz are encoded only poorly by TCs of either signalling mode [35]. This feature is only partly attributable to neuronal noise.…”

Section: The Performance Of Spiking and Graded Potential Neurons In Ementioning

confidence: 89%

“…This feature is only partly attributable to neuronal noise. It is also the consequence of the fact that the stimulus-induced responses of TCs do not only depend on pattern velocity but also on its higher temporal derivatives [35,36]. In a recent study of a TC that generates both graded potential changes and spike-like events, it was concluded that more information about stimulus velocity is encoded by the graded response than by the spike-like events [37 • ].…”

Section: The Performance Of Spiking and Graded Potential Neurons In Ementioning

confidence: 99%

“…On the other hand, the reliability of motion encoding has now been quantified using data analysis approaches derived from signal detection and information theory [33][34][35]39 • ,40,41 •• , 49-52,53 • ,54]. Although these approaches have been important for analysing neuronal performance, their limitations have to be kept in mind.…”

Section: Encoding Of Motion In Real Time By the Fly Visual System Egementioning

confidence: 99%

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