Two-photon imaging and analysis of neural network dynamics

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behaviour. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so called 'microcircuits') remains comparably poor. In large parts, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near- millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress in Physic

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

High-speed high-resolution plasma spectroscopy using spatial-multiplex coherence imaging techniques

We have recently obtained simultaneous two-dimensional (2D) plasmaDoppler spectroscopic images of plasma brightness, temperature, and flow fields. Using compact polarization optical methods, quadrature images of the optical coherence of an isolated spectral line are multiplexed to four quadrants of a fast charge-coupled device camera. The simultaneously captured, but distinct, images can be simply processed to unfold the plasma brightness, temperature, and flow fields. This static system, which is a spatial-multiplex variant of previously reported electro-optically modulated, temporal-multiplex coherence imaging systems, is based on a high-throughput imagingpolarizationinterferometer that employs crossed Wollaston prisms and appropriate image plane masks. Because the images are captured simultaneously, it is well suited to high-spectral-resolution, high-throughput 2D imaging of transient or rapidly changing spectroscopic scenes. To illustrate instrument performance we present recent results using a static 4-quadrant Dopplercoherence imaging on the H-1 heliac at the ANU.This work has been, in part, supported by the Australian Government Department of Education, Science and Training under the International Science Linkages program, Grant No. CG050061

Probing the cosmic web: inter-cluster filament detection using gravitational lensing

The problem of detecting dark matter filaments in the cosmic web is considered. Weak lensing is an ideal probe of dark matter, and therefore forms the basis of particularly promising detection methods. We consider and develop a number of weak lensing techniques that could be used to detect filaments in individual or stacked cluster fields, and apply them to synthetic lensing data sets in the fields of clusters from the Millennium Simulation. These techniques are multipole moments of the shear and convergence, mass reconstruction, and parameterized fits to filament mass profiles using a Markov Chain Monte Carlo approach. In particular, two new filament detection techniques are explored (multipole shear filters and Markov Chain Monte Carlo mass profile fits), and we outline the quality of data required to be able to identify and quantify filament profiles. We also consider the effects of large scale structure on filament detection. We conclude that using these techniques, there will be realistic prospects of detecting filaments in data from future space-based missions. The methods presented in this paper will be of great use in the identification of dark matter filaments in future surveys.Comment: 12 pages, 4 figures, MNRAS accepted, (replacement due to corrupted end of pdf file