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.

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.

Kurtz, Rafael, Volker Dü rr, and Martin Egelhaaf. Dendritic calcium accumulation associated with direction-selective adaptation in visual motion-sensitive neurons in vivo. J Neurophysiol 84: 1914 -1923, 2000. Motion adaptation in directionally selective tangential cells (TC) of the fly visual system has previously been explained as a presynaptic mechanism. Based on the observation that adaptation is in part direction selective, which is not accounted for by the former models of motion adaptation, we investigated whether physiological changes located in the TC dendrite can contribute to motion adaptation. Visual motion in the neuron's preferred direction (PD) induced stronger adaptation than motion in the opposite direction and was followed by an afterhyperpolarization (AHP). The AHP subsides in the same time as adaptation recovers. By combining in vivo calcium fluorescence imaging with intracellular recording, we show that dendritic calcium accumulation following motion in the PD is correlated with the AHP. These results are consistent with a calcium-dependent physiological change in TCs underlying adaptation during continuous stimulation with PD motion, expressing itself as an AHP after the stimulus stops. However, direction selectivity of adaptation is probably not solely related to a calcium-dependent mechanism because direction-selective effects can also be observed for fast moving stimuli, which do not induce sizeable calcium accumulation. In addition, a comparison of two classes of TCs revealed differences in the relationship of calcium accumulation and AHP when the stimulus velocity was varied. Thus the potential role of calcium in motion adaptation depends on stimulation parameters and cell class.

Synaptic transmission between a graded potential neuron and a spiking neuron was investigated in vivo using sensory stimulation instead of artificial excitation of the presynaptic neuron. During visual motion stimulation, individual presynaptic and postsynaptic neurons in the brain of the fly were electrophysiologically recorded together with concentration changes of presynaptic calcium (Delta[Ca(2+)](pre)). Preferred-direction motion leads to depolarization of the presynaptic neuron. It also produces pronounced increases in [Ca(2+)](pre) and the postsynaptic spike rate. Motion in the opposite direction was associated with hyperpolarization of the presynaptic cell but only a weak reduction in [Ca(2+)](pre) and the postsynaptic spike rate. Apart from this rectification, the relationships between presynaptic depolarizations, Delta[Ca(2+)](pre), and postsynaptic spike rates are, on average, linear over the entire range of activity levels that can be elicited by sensory stimulation. Thus, the inevitably limited range in which the gain of overall synaptic signal transfer is constant appears to be adjusted to sensory input strengths.

Abstract-Synaptic transmission is usually studied in vitro with electrical stimulation replacing the natural input of the system. In contrast, we analyzed in vivo transfer of visual motion information from graded-potential presynaptic to spiking postsynaptic neurons in the fly. Motion in the null direction leads to hyperpolarization of the presynaptic neuron but does not much influence the postsynaptic cell, because its firing rate is already low during rest, giving only little scope for further reductions. In contrast, preferred-direction motion leads to presynaptic depolarizations and increases the postsynaptic spike rate. Signal transfer to the postsynaptic cell is linear and reliable for presynaptic graded membrane potential fluctuations of up to approximately 10 Hz. This frequency range covers the dynamic range of velocities that is encoded with a high gain by visual motionsensitive neurons. Hence, information about preferred-direction motion is transmitted largely undistorted ensuring a consistent dependency of neuronal signals on stimulus parameters, such as motion velocity. Postsynaptic spikes are often elicited by rapid presynaptic spike-like depolarizations which superimpose the graded membrane potential. Although the timing of most of these spike-like depolarizations is set by noise and not by the motion stimulus, it is preserved at the synapse with millisecond precision.