Numerical investigation of wing morphing capabilities applied to a Horten type swept wing geometry

The inspiration for this work has been derived from the work done by Lippisch, the Hortens, Northrop and largely by the flight of birds in nature. Swept-wing tailless aircraft have been in vogue since World War-II and have now taken on a new role in stealth warfare primarily due to their low radar signatures. They also exhibit a highly efficient aerodynamic configuration with low parasitic drag. However, conventional tailless aircraft suffer from a lack of proper control mechanisms and have thus been forced to compromise their efficiency for better control. This work done at WVU aims to introduce a morphing mechanism for better control of tailless aircraft1. This morphing mechanism will provide for variable twist capability of the wing and can theoretically provide better roll, pitch and yaw control for a tailless aircraft. This research is intended to give us a better understanding of the flow physics that are encountered during the various morphed stages of flight and compares them to conventional geometries. A three dimensional model of a conventional and morphed wing was simulated using an inviscid panel code method at various stages of flight, and the results were compared to actual wind tunnel data. The study looks at the coefficients of lift, induced drag and the various moments encountered. Preliminary studies indicate wing morphing as a suitable candidate for more efficient flight.;1This work was funded by NASA-Dryden flight research center

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for reconstruction is the detection of neuronal boundaries. Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension. For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection. Here we achieve a substantial gain in accuracy through three innovations. Following the trend towards deeper networks for object recognition, we use a much deeper network than previously employed for boundary detection. Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context. Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map. Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed. Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem

Micro-technologies to constrain neuronal networks

Micro-technologies broadly encompass a range of technologies that deal with the developmentof tools on the order of a few microns. These tools have made steady inroads into traditionalbiology and have helped probe the functioning of cells on the order of tens of microns.The objective of this work was to use engineering techniques to ask specific questions inneuroscience.Using two different techniques, namely microcontact printing and microfluidics we suc-cessfully restricted the spread of networks of neurons to defined geometries. In the formercase, we chose to restrict networks to 'ring' shaped geometries, in order to study emergentreverberating properties in the resulting network. Ring shaped neuronal networks displayedreverberatory activity upon brief stimulation. This reverberatory activity was enhancedwhen network inhibition was abolished pharmacologically. Finally the effect of varying geometric parameters on this form of network activity was assessed. Here we found that smallchanges in the geometry did not have any significant effect on the reverberatory activity.In the second case, we restricted networks of neurons inside microfluidic devices. Thesemicrofluidic devices were capable of maintaining two populations of neurons in a fluidicallyisolated manner. The two populations communicated via microgrooves that allowed axons to reach across either population. We integrated an electrophysiological framework onto themicrofluidic device such that one of the two populations could be electrically stimulated. Weshow, using calcium imaging it was possible to stimulate neurons inside these devices.In conclusion, we have demonstrated the use of micro-technologies to constrain neuronalnetworks to specific geometries. We show here the emergence of reverberation in 'ring'shaped networks. Finally, we also created a novel microfluidic platform to culture neuronsfor extended periods of time

Methods for Mapping Neuronal Activity to Synaptic Connectivity: Lessons From Larval Zebrafish

For a mechanistic understanding of neuronal circuits in the brain, a detailed description of information flow is necessary. Thereby it is crucial to link neuron function to the underlying circuit structure. Multiphoton calcium imaging is the standard technique to record the activity of hundreds of neurons simultaneously. Similarly, recent advances in high-throughput electron microscopy techniques allow for the reconstruction of synaptic resolution wiring diagrams. These two methods can be combined to study both function and structure in the same specimen. Due to its small size and optical transparency, the larval zebrafish brain is one of the very few vertebrate systems where both, activity and connectivity of all neurons from entire, anatomically defined brain regions, can be analyzed. Here, we describe different methods and the tools required for combining multiphoton microscopy with dense circuit reconstruction from electron microscopy stacks of entire brain regions in the larval zebrafish

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit

Neuronal wiring diagrams reconstructed by electron microscopy1,2,3,4,5 pose new questions about the organization of nervous systems following the time-honored tradition of cross-species comparisons.6,7 The C. elegans connectome has been conceptualized as a sensorimotor circuit that is approximately feedforward,8,9,10,11 starting from sensory neurons proceeding to interneurons and ending with motor neurons. Overrepresentation of a 3-cell motif often known as the "feedforward loop" has provided further evidence for feedforwardness.10,12 Here, we contrast with another sensorimotor wiring diagram that was recently reconstructed from a larval zebrafish brainstem.13 We show that the 3-cycle, another 3-cell motif, is highly overrepresented in the oculomotor module of this wiring diagram. This is a first for any neuronal wiring diagram reconstructed by electron microscopy, whether invertebrate12,14 or mammalian.15,16,17 The 3-cycle of cells is "aligned" with a 3-cycle of neuronal groups in a stochastic block model (SBM)18 of the oculomotor module. However, the cellular cycles exhibit more specificity than can be explained by the group cycles-recurrence to the same neuron is surprisingly common. Cyclic structure could be relevant for theories of oculomotor function that depend on recurrent connectivity. The cyclic structure coexists with the classic vestibulo-ocular reflex arc for horizontal eye movements,19 and could be relevant for recurrent network models of temporal integration by the oculomotor system.20,21