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.

Sensory coding has long been discussed in terms of a dichotomy between spike timing and rate coding. However, recent studies found that in primate mechanoperception and other sensory systems, spike rates and timing of cell populations complement each other. They simultaneously carry information about different stimulus properties in a multiplexed way. Here, we present evidence for multiplexed encoding of tactile skin stimulation in the tiny population of leech mechanoreceptors, consisting of only 10 cells of two types with overlapping receptive fields. Each mechanoreceptor neuron of the leech varies spike count and response latency to both touch intensity and location, leading to ambiguous responses to different stimuli. Nevertheless, three different stimulus estimation techniques consistently reveal that the neuronal population allows reliable decoding of both stimulus properties. For the two mechanoreceptor types, the transient responses of T (touch) cells and the sustained responses of P (pressure) cells, the relative timing of the first spikes of two mechanoreceptors encodes stimulus location, whereas summed spike counts represent touch intensity. Differences between the cell types become evident in responses to combined stimulus properties. The best estimation performance for stimulus location is obtained from the relative first spike timing of the faster and temporally more precise T cells. Simultaneously, the sustained responses of P cells indicate touch intensity by summed spike counts and stimulus duration by the duration of spike responses. The striking similarities of these results with previous findings on primate mechanosensory afferents suggest multiplexed population coding as a general principle of somatosensation.

Measurement of the optomotor response is a common way to determine thresholds of the visual system in animals. Particularly in mice, it is frequently used to characterize the visual performance of different genetically modified strains or to test the effect of various drugs on visual performance. Several methods have been developed to facilitate the presentation of stimuli using computer screens or projectors. Common methods are either based on the measurement of eye movement during optokinetic reflex behavior or rely on the measurement of head and/or body-movements during optomotor responses. Eye-movements can easily and objectively be quantified, but their measurement requires invasive fixation of the animals. Head movements can be observed in freely moving animals, but until now depended on the judgment of a human observer who reported the counted tracking movements of the animal during an experiment. In this study we present a novel measurement and stimulation system based on open source building plans and software. This system presents appropriate 360 stimuli while simultaneously video-tracking the animal's head-movements without fixation. The on-line determined head gaze is used to adjust the stimulus to the head position, as well as to automatically calculate visual acuity. Exemplary, we show that automatically measured visual response curves of mice match the results obtained by a human observer very well. The spatial acuity thresholds yielded by the automatic analysis are also consistent with the human observer approach and with published results. Hence, OMR-arena provides an affordable, convenient and objective way to measure mouse visual performance.

Many sensory neurons encode temporal information by detecting coincident arrivals of synaptic inputs. In the mammalian auditory brainstem, binaural neurons of the medial superior olive (MSO) are known to act as coincidence detectors, whereas in the lateral superior olive (LSO) roles of coincidence detection have remained unclear. LSO neurons receive excitatory and inhibitory inputs driven by ipsilateral and contralateral acoustic stimuli, respectively, and vary their output spike rates according to interaural level differences. In addition, LSO neurons are also sensitive to binaural phase differences of low-frequency tones and envelopes of amplitude-modulated (AM) sounds. Previous physiological recordings in vivo found considerable variations in monaural AM-tuning across neurons. To investigate the underlying mechanisms of the observed temporal tuning properties of LSO and their sources of variability, we used a simple coincidence counting model and examined how specific parameters of coincidence detection affect monaural and binaural AM coding. Spike rates and phase-locking of evoked excitatory and spontaneous inhibitory inputs had only minor effects on LSO output to monaural AM inputs. In contrast, the coincidence threshold of the model neuron affected both the overall spike rates and the half-peak positions of the AM-tuning curve, whereas the width of the coincidence window merely influenced the output spike rates. The duration of the refractory period affected only the low-frequency portion of the monaural AM-tuning curve. Unlike monaural AM coding, temporal factors, such as the coincidence window and the effective duration of inhibition, played a major role in determining the trough positions of simulated binaural phase-response curves. In addition, empirically-observed level-dependence of binaural phase-coding was reproduced in the framework of our minimalistic coincidence counting model. These modeling results suggest that coincidence detection of excitatory and inhibitory synaptic inputs is essential for LSO neurons to encode both monaural and binaural AM sounds.