Reconstruction of Virtual Neural Circuits in an Insect Brain

The reconstruction of large-scale nervous systems represents a major scientific and engineering challenge in current neuroscience research that needs to be resolved in order to understand the emergent properties of such systems. We focus on insect nervous systems because they represent a good compromise between architectural simplicity and the ability to generate a rich behavioral repertoire. In insects, several sensory maps have been reconstructed so far. We provide an overview over this work including our reconstruction of population activity in the primary olfactory network, the antennal lobe. Our reconstruction approach, that also provides functional connectivity data, will be refined and extended to allow the building of larger scale neural circuits up to entire insect brains, from sensory input to motor output

Asymmetric ephaptic inhibition between compartmentalized olfactory receptor neurons.

In the Drosophila antenna, different subtypes of olfactory receptor neurons (ORNs) housed in the same sensory hair (sensillum) can inhibit each other non-synaptically. However, the mechanisms underlying this underexplored form of lateral inhibition remain unclear. Here we use recordings from pairs of sensilla impaled by the same tungsten electrode to demonstrate that direct electrical ("ephaptic") interactions mediate lateral inhibition between ORNs. Intriguingly, within individual sensilla, we find that ephaptic lateral inhibition is asymmetric such that one ORN exerts greater influence onto its neighbor. Serial block-face scanning electron microscopy of genetically identified ORNs and circuit modeling indicate that asymmetric lateral inhibition reflects a surprisingly simple mechanism: the physically larger ORN in a pair corresponds to the dominant neuron in ephaptic interactions. Thus, morphometric differences between compartmentalized ORNs account for highly specialized inhibitory interactions that govern information processing at the earliest stages of olfactory coding

Vibration-Processing Interneurons in the Honeybee Brain

The afferents of the Johnston's organ (JO) in the honeybee brain send their axons to three distinct areas, the dorsal lobe, the dorsal subesophageal ganglion (DL-dSEG), and the posterior protocerebral lobe (PPL), suggesting that vibratory signals detected by the JO are processed differentially in these primary sensory centers. The morphological and physiological characteristics of interneurons arborizing in these areas were studied by intracellular recording and staining. DL-Int-1 and DL-Int-2 have dense arborizations in the DL-dSEG and respond to vibratory stimulation applied to the JO in either tonic excitatory, on-off-phasic excitatory, or tonic inhibitory patterns. PPL-D-1 has dense arborizations in the PPL, sends axons into the ventral nerve cord (VNC), and responds to vibratory stimulation and olfactory stimulation simultaneously applied to the antennae in long-lasting excitatory pattern. These results show that there are at least two parallel pathways for vibration processing through the DL-dSEG and the PPL. In this study, Honeybee Standard Brain was used as the common reference, and the morphology of two types of interneurons (DL-Int-1 and DL-Int-2) and JO afferents was merged into the standard brain based on the boundary of several neuropiles, greatly supporting the understanding of the spatial relationship between these identified neurons and JO afferents. The visualization of the region where the JO afferents are closely appositioned to these DL interneurons demonstrated the difference in putative synaptic regions between the JO afferents and these DL interneurons (DL-Int-1 and DL-Int-2) in the DL. The neural circuits related to the vibration-processing interneurons are discussed

Analysis and network simulations of honeybee interneurons responsive to waggle dance vibration signals

BACKGROUND: Honeybees have long fascinated neuroscientists with their highly evolved social structure and rich behavioral repertoire. They sense air vibrations with their antennae, which is vital for several activities during foraging, like waggle dance communication and flight. GOALS: This thesis presents the investigation of the function of an identified vibration-sensitive interneuron, DL-Int-1. Primary goals were the investigation of (i) adaptations during maturation and (ii) the role of DL-Int-1 in networks encoding distance information of waggle dance vibration signals. RESULTS: Visual inspection indicated that DL-Int-1 morphologies had similar gross structure, but were translated, rotated and scaled relative to each other. To enable detailed spatial comparison, an algorithm for the spatial co-registration of neuron morphologies, Reg-MaxS-N was developed and validated. Experimental data from DL-Int-1 was provided by our Japanese collaborators. Comparison of morphologies from newly emerged adult and forager DL-Int-1 revealed minor changes in gross dendritic features and consistent, region-dependent and spatially localized changes in dendritic density. Comparison of electrophysiological response properties showed an increase in firing rate differences between stimulus and non-stimulus periods during maturation. A putative disinhibitory network in the honeybee primary auditory center was proposed based on experimental evidence. Simulations showed that the network was consistent with experimental observations and clarified the central inhibitory role of DL-Int-1 in shaping the network output. RELEVANCE: Reg-MaxS-N presents a novel approach for the spatial co-registration of morphologies. Adaptations in DL-Int-1 morphology during maturation indicate improved connectivity and signal propagation. The central role of DL-Int-1 in a disinhibitory network in the honeybee primary auditory center combined with adaptions in its response properties during maturation could indicate better encoding of distance information from waggle dance vibration sig- nals

Flight of the dragonflies and damselflies

This work is a synthesis of our current understanding of the mechanics, aerodynamics and visually mediated control of dragonfly and damselfly flight, with the addition of new experimental and computational data in several key areas. These are: the diversity of dragonfly wing morphologies, the aerodynamics of gliding flight, force generation in flapping flight, aerodynamic efficiency, comparative flight performance and pursuit strategies during predatory and territorial flights. New data are set in context by brief reviews covering anatomy at several scales, insect aerodynamics, neuromechanics and behaviour. We achieve a new perspective by means of a diverse range of techniques, including laser-line mapping of wing topographies, computational fluid dynamics simulations of finely detailed wing geometries, quantitative imaging using particle image velocimetry of on-wing and wake flow patterns, classical aerodynamic theory, photography in the field, infrared motion capture and multi-camera optical tracking of free flight trajectories in laboratory environments. Our comprehensive approach enables a novel synthesis of datasets and subfields that integrates many aspects of flight from the neurobiology of the compound eye, through the aeromechanical interface with the surrounding fluid, to flight performance under cruising and higher-energy behavioural modes

One rule to grow them all: A general theory of neuronal branching and its practical application

Understanding the principles governing axonal and dendritic branching is essential for unravelling the functionality of single neurons and the way in which they connect. Nevertheless, no formalism has yet been described which can capture the general features of neuronal branching. Here we propose such a formalism, which is derived from the expression of dendritic arborizations as locally optimized graphs. Inspired by Ramon y Cajal's laws of conservation of cytoplasm and conduction time in neural circuitry, we show that this graphical representation can be used to optimize these variables. This approach allows us to generate synthetic branching geometries which replicate morphological features of any tested neuron. The essential structure of a neuronal tree is thereby captured by the density profile of its spanning field and by a single parameter, a balancing factor weighing the costs for material and conduction time. This balancing factor determines a neuron's electrotonic compartmentalization. Additions to this rule, when required in the construction process, can be directly attributed to developmental processes or a neuron's computational role within its neural circuit. The simulations presented here are implemented in an open-source software package, the "TREES toolbox," which provides a general set of tools for analyzing, manipulating, and generating dendritic structure, including a tool to create synthetic members of any particular cell group and an approach for a model-based supervised automatic morphological reconstruction from fluorescent image stacks. These approaches provide new insights into the constraints governing dendritic architectures. They also provide a novel framework for modelling and analyzing neuronal branching structures and for constructing realistic synthetic neural networks

The morphological identity of insect dendrites

Dendrite morphology is the most prominent feature of nerve cells, investigated since the origins of modern neuroscience. The last century of neuroanatomical research has revealed an overwhelming diversity of different dendritic shapes and complexities. Its great variability, however, largely interferes with understanding the underlying principles of neuronal wiring and its functional implications. This work addresses this issue by studying a morphological and functional exception- ally conserved network of neurons located in the visual system of flies. Lobula Plate Tangential Cells (LPTCs) have been shown to compute motion vision and contribute to the impressive flight capabilities of flies. Cells of this system exhibit a high degree of constancy in topographic location, morphology and function over all individuals of one species. This constancy allows investigation of functionally identical cells over a large population of flies, and therefore potentially to truly understand the underlying principles of their morphologies. Supported by a large database of in vivo cell reconstructions and a computational quantification framework, it was possible to uncover some of those principles of LPTC anatomy. We show that the key to the cells’ morphological identity lies in the size and shape of the area they span into. Their detailed branching structure and topology is then merely a result of a common growth program shared by all analyzed cells. Application of a previously published branching theory confirmed this finding. When grown into the spanning fields obtained from the in vivo cell reconstruction, artificial cells could be synthesized that resembled all anatomical properties that characterize their natural counterparts. Furthermore, the morphological comparison of the same identified cells in Calliphora and Drosophila allowed to study a functionally conserved system under the influence of extensive down-scaling. The huge size reduction did not affect the underlying branching principles: Drosophila LPTCs followed the very same rules as their Calliphora coun- terparts. On the other hand, we observed significant differences in complexity and relative diameter scaling. An electrotonic analysis revealed that these differences can be explained by a common functional architecture implemented in the LPTCs of both species. Finally, we could modify the LPTC neuronal interaction behavior thanks to the genetical accessibility of Drosophila’s wiring program. The transmembrane protein family Dscam has been shown to mediate the process of adhesion and repulsion of neurites. By manipulating the molecular Dscam profile in Drosophila LPTCs it was possible to change their morphological expansion. The low variability of the LPTCs spanning field in wild type flies and their two-dimensional extension allowed to thoroughly map these morphological alterations in flies with Dscam modifications. In line with the LPTCs retinotopic input arrangement, electrophysiological experiments yielded an inherent linear relationship of their locally reduced dendritic coverage and their locally reduced stimulus sensitivity. With this work I hope to contribute to the general understanding of neuronal morphology of LPTCs and to present a valuable workflow for the analysis of neuronal structure

Neuron Depot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications

Neuroscience today deals with a "data deluge" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations