Development and application of a neuroinformatics environment for neuroscience and neuroethology

NeuronDepot: 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.

Development of a scheme and tools to construct a standard moth brain for neural network simulations

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies.

Heterogeneity in dendritic morphology of moth antennal lobe projection neurons

A population of projection neurons (PNs) in the antennal lobe (AL) integrates sensory information from the antenna and is essential for processing odor information in the insect brain. We examined the anatomy of this neuronal population in the brain of the silkmoth Bombyx mori. Using intracellular dye injection, we labeled a total of 246 PNs and systematically analyzed their morphological features, including the soma position, antennocerebral tract, and number of innervating glomeruli. For example, we analyzed PNs that had somata in the different cell clusters, innervated overlapping but different groups of glomeruli, and ran through different pathways. We also identified glomeruli innervated by PNs using a previously established procedure that first classifies glomeruli into regional groups and then identifies individual glomeruli. We analyzed uniglomerular PNs (75.6% of the total) and found heterogeneity in the dendritic morphology of the PNs that was dependent on the regions and/or the innervating glomeruli. For example, most PNs innervating the macroglomerular complex did not have extraglomerular processes, whereas most PNs innervating ordinary glomeruli did. Moreover, PNs innervating the toroid glomerulus showed heterogeneity in their dendritic morphology. These PNs had dendritic arborization in different areas within the glomerulus. We found that, in some cases, the innervation pattern of the PN dendrite correlated with individual variation in the glomerular organization. These results indicate that PNs are not homogeneous populations, and in some cases morphological heterogeneity in PNs correlated with change in glomerular organization in the silkmoth AL.

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.

Constancy and variability of glomerular organization in the antennal lobe of the silkmoth

We investigated the anatomical organization of glomeruli in the antennal lobes (ALs) of male silkmoths. We reconstructed 10 different ALs and established an identification procedure for individual glomeruli by using size, shape, and position relative to anatomical landmarks. Quantitative analysis of these morphological characteristics supported the validity of our identification strategy. The glomerular organization of the ALs was roughly conserved between different ALs. However, we found individual variations that were reproducibly observed. The combination of a digital atlas with other experimental techniques, such as electrophysiology, optical imaging, and genetics, should facilitate a more in-depth analysis of sensory information processing in silkmoth ALs.