1. While collecting nectar in hovering flight the European hawk moth Macroglossum stellatarum efficiently regulates its distance relative to flowers that are shaken by wind. This can be demonstrated in laboratory experiments by moving dummy flowers (blue cardboard disks) towards and away from the feeding animal ( Fig. 1).2. Distance regulation is predominantly mediated by visual cues. Mechanoreceptors on the proboscis appear to contribute little to the response.3. Movements of dummy flowers can be simulated by expanding and contracting a pattern projected onto a screen. With this technique we investigated the dynamical properties of the servo mechanism underlying distance regulation. The system behaves as a bandpass filter with corner frequencies of 0.15 and 5 Hz (Figs.2,3).4. When a high-speed ramp-like movement of the flower is simulated, there is an asymmetry in the response. During simulated approach the reaction is phasic-tonic with a pronounced overshoot at the beginning, during simulated retraction it remains tonic (Fig.5B,C).5. During distance regulation the animals compensate for the speed of the edge of the projected pattern. Distance regulation improves substantially when the number of stimulated elementary movement detectors is increased through increasing the number of contour lines by projecting concentric rings instead of a homogeneous disk (Figs.7, 8).

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We present a general framework for the reconstruction of natural video scenes encoded with a population of spiking neural circuits with random thresholds. The natural scenes are modeled as space-time functions that belong to a space of trigonometric polynomials. The visual encoding system consists of a bank of filters, modeling the visual receptive fields, in cascade with a population of neural circuits, modeling encoding in the early visual system. The neuron models considered include integrate-and-fire neurons and ON-OFF neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed to be random. We demonstrate that neural spiking is akin to taking noisy measurements on the stimulus both for time-varying and space-time-varying stimuli. We formulate the reconstruction problem as the minimization of a suitable cost functional in a finite-dimensional vector space and provide an explicit algorithm for stimulus recovery. We also present a general solution using the theory of smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both synthetic video as well as for natural scenes and demonstrate that the quality of the reconstruction degrades gracefully as the threshold variability of the neurons increases.

In recent years, a wealth of Drosophila neuroscience data have become available including cell type, connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison and evaluation of circuit functions of the fruit fly brain.

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