Adaptation of transient responses of a movement-sensitive neuron in the visual system of the blowfly Calliphora erythrocephala

Steveninck, R R de Ruter van; Zaagman, W. H.; Mastebroek, H. A. K.

doi:10.1007/bf00318418

Cited by 108 publications

(103 citation statements)

References 26 publications

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“…When the visual system of an intact animal is exposed to strong motion stimuli and subsequently probed with small impulses or steps in velocity, the response of a tangential cell to these test stimuli is reduced relative to that of an initially unstimulated cell (Harris et al, 2000;Clifford and Langley, 1996;de Ruyter van Steveninck et al, 1986;Maddess and Laughlin, 1985). This phenomenon has been termed motion adaptation.…”

Section: Motion Adaptationmentioning

confidence: 99%

“…This phenomenon has been termed motion adaptation. While in the past it has been suggested that its mechanism is a reduction in the time constant of the delay operator in a correlational EMD (Clifford et al, 1997;Borst and Egelhaaf, 1987;de Ruyter van Steveninck et al, 1986), recent work has produced strong evidence that this is not the case, and that it is actually due to a reduction in gain somewhere in the prior signal processing pathway, as well as a shift in the resting membrane potential of the cell when the adapting motion is in a direction that causes net excitation of the cell (Harris et al, 2000;Harris et al, 1999). The gain reduction is elicited by motion in any direction, even when it causes little response or net inhibition in the cell.…”

Section: Motion Adaptationmentioning

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: Motion Adaptationmentioning

confidence: 99%

Section: Motion Adaptationmentioning

confidence: 99%

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

2005

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“…Moreover, the properties of fly TCs change as a result of stimulus history [63][64][65][66][67][68][69]. Although the functional significance of these adaptational processes is debated, they may Either synchronously recorded responses were used (blue trace) or responses that were not recorded synchronously but obtained from repetitive presentation with the same motion stimulus (red trace).…”

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

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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“…Indeed, a major inhibitory element of the FD1-cell (Warzecha et al 1993) has been shown to be activated above its resting-level during translatory motion (Egelhaaf et al 1993) and to exhibit transient response properties (DuÈ rr 1998). Alternatively, the in¯uence of background motion on the temporal response properties of the FD1b-cell might also be explained by the fact that the FD1b-cell is in a di erent adaptational state (Maddess and Laughlin 1985;de Ruyter van Steveninck et al 1986;Borst and Egelhaaf 1987;Harris et al 1999) during background motion than in situations without background motion. Irrespective of the di erent possible explanations, the responses of the FD1b-cell to object motion become more transient during translatory background motion compared with object motion in front of a stationary background.…”

Section: Identi®cation Of Cellsmentioning

confidence: 99%