Behavioral responses to wind are thought to play a critical role in controlling the dispersal and population genetics of wild Drosophila species1,2, as well as their navigation in flight3, but their underlying neurobiological basis is unknown. We show that Drosophila melanogaster, like wild-caught Drosophila strains4, exhibits robust wind-induced suppression of locomotion (WISL), in response to air currents delivered at speeds normally encountered in nature1,2. Here we identify wind-sensitive neurons in Johnston’s Organ (JO), an antennal mechanosensory structure previously implicated in near-field sound detection (reviewed in5,6). Using Gal4 lines targeted to different subsets of JO neurons7, and a genetically encoded calcium indicator8, we show that wind and near-field sound (courtship song) activate distinct populations of JO neurons, which project to different regions of the antennal and mechanosensory motor center (AMMC) in the central brain. Selective genetic ablation of wind-sensitive JO neurons in the antenna abolishes WISL behavior, without impairing hearing. Different neuronal subsets within the wind-sensitive population, moreover, respond to different directions of arista deflection caused by airflow and project to different regions of the AMMC, providing a rudimentary map of wind-direction in the brain. Importantly, sound- and wind-sensitive JO neurons exhibit different intrinsic response properties: the former are phasically activated by small, bi-directional, displacements of the aristae, while the latter are tonically activated by unidirectional, static deflections of larger magnitude. These different intrinsic properties are well suited to the detection of oscillatory pulses of near-field sound and laminar airflow, respectively. These data identify wind-sensitive neurons in JO, a structure that has been primarily associated with hearing, and reveal how the brain can distinguish different types of air particle movements, using a common sensory organ.

No abstract

The robust representation of the environment from unreliable sensory cues is vital for the efficient function of the brain. However, how the neural processing captures the most reliable cues is unknown. The interaural time difference (ITD) is the primary cue to localize sound in horizontal space. ITD is encoded in the firing rate of neurons that detect interaural phase difference (IPD). Due to the filtering effect of the head, IPD for a given location varies depending on the environmental context. We found that, in barn owls, at each location there is a frequency range where the head filtering yields the most reliable IPDs across contexts. Remarkably, the frequency tuning of space-specific neurons in the owl's midbrain varies with their preferred sound location, matching the range that carries the most reliable IPD. Thus, frequency tuning in the owl's space-specific neurons reflects a higher-order feature of the code that captures cue reliability.DOI: http://dx.doi.org/10.7554/eLife.04854.001

Optimal use of sensory information requires that the brain estimates the reliability of sensory cues, but the neural correlate of cue reliability relevant for behavior is not well defined. Here, we addressed this issue by examining how the reliability of spatial cue influences neuronal responses and behavior in the owl's auditory system. We show that the firing rate and spatial selectivity changed with cue reliability due to the mechanisms generating the tuning to the sound localization cue. We found that the correlated variability among neurons strongly depended on the shape of the tuning curves. Finally, we demonstrated that the change in the neurons' selectivity was necessary and sufficient for a network of stochastic neurons to predict behavior when sensory cues were corrupted with noise. This study demonstrates that the shape of tuning curves can stand alone as a coding dimension of environmental statistics.

Interaural time difference (ITD) plays a central role in many auditory functions, most importantly in sound localization. The classic model for how ITD is computed was put forth by Jeffress (1948). One of the predictions of the Jeffress model is that the neurons that compute ITD should behave as cross-correlators. Whereas cross-correlation-like properties of the ITD-computing neurons have been reported, attempts to show that the shape of the ITD response function is determined by the spectral tuning of the neuron, a core prediction of cross-correlation, have been unsuccessful. Using reverse correlation analysis, we demonstrate in the barn owl that the relationship between the spectral tuning and the ITD response of the ITD-computing neurons is that predicted by cross-correlation. Moreover, we show that a model of coincidence detector responses derived from responses to binaurally uncorrelated noise is consistent with binaural interaction based on cross-correlation. These results are thus consistent with one of the key tenets of the Jeffress model. Our work sets forth both the methodology to answer whether cross-correlation describes coincidence detector responses and a demonstration that in the barn owl, the result is that expected by theory.