Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

We reported initial work towards a new fast and scalable scientific visualization technology that leverages the Vulkan API to achieve unprecedented performance through GPUs. This technology is implemented in a C/C++ library called Datoviz that offers an intermediate-level API for scientific visualization libraries and software. Datoviz provides a unified graphics stack for 2-D, 3-D, graphical user interfaces, and natively supports efficient interactions between rendering and general-purpose GPU computing. A major direction of development is to investigate the integration of Datoviz as a low-level backend of a future version of VisPy, a popular Python scientific plotting librar

Enhancement of Optical Coherence Tomography Images of the Retina by Normalization and Fusion

This paper describes an image processing method applied to Optical Coherence Tomography (OCT) images of the retina. The aim is to achieve improved OCT images from the fusion of sequential OCT scans obtained at identical retinal locations. The method is based on the normalization of the acquired images and their fusion. As a result, a noise reduction and an image enhancement are reached. Thanks to the resulting improvement in retinal imaging, clinical specialists are able to evaluate more efficiently eyes pathologies and anomalies. This paper presents the proposed method and gives some evaluation results

Toward Guidelines for Research on Human Embryo Models Formed from Stem Cells.

Over the past few years, a number of research groups have reported striking progress on the generation of in vitro models from mouse and human stem cells that replicate aspects of early embryonic development. Not only do these models reproduce some key cell fate decisions but, especially in the mouse system, they also mimic the spatiotemporal arrangements of embryonic and extraembryonic tissues that are required for developmental patterning and implantation in the uterus. If such models could be developed for the early human embryo, they would have great potential benefits for understanding early human development, for biomedical science, and for reducing the use of animals and human embryos in research. However, guidelines for the ethical conduct of this line of work are at present not well defined. In this Forum article, we discuss some key aspects of this emerging area of research and provide some recommendations for its ethical oversight

Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

High resolution ultrasound-guided microinjection for interventional studies of early embryonic and placental development in vivo in mice

BACKGROUND: In utero microinjection has proven valuable for exploring the developmental consequences of altering gene expression, and for studying cell lineage or migration during the latter half of embryonic mouse development (from embryonic day 9.5 of gestation (E9.5)). In the current study, we use ultrasound guidance to accurately target microinjections in the conceptus at E6.5–E7.5, which is prior to cardiovascular or placental dependence. This method may be useful for determining the developmental effects of targeted genetic or cellular interventions at critical stages of placentation, gastrulation, axis formation, and neural tube closure. RESULTS: In 40 MHz ultrasound images at E6.5, the ectoplacental cone region and proamniotic cavity could be visualized. The ectoplacental cone region was successfully targeted with 13.8 nL of a fluorescent bead suspension with few or no beads off-target in 51% of concepti microinjected at E6.5 (28/55 injected). Seventy eight percent of the embryos survived 2 to 12 days post injection (93/119), 73% (41/56) survived to term of which 68% (38/56) survived and appeared normal one week after birth. At E7.5, the amniotic and exocoelomic cavities, and ectoplacental cone region were discernable. Our success at targeting with few or no beads off-target was 90% (36/40) for the ectoplacental cone region and 81% (35/43) for the exocoelomic cavity but tended to be less, 68% (34/50), for the smaller amniotic cavity. At E11.5, beads microinjected at E7.5 into the ectoplacental cone region were found in the placental spongiotrophoblast layer, those injected into the exocoelomic cavity were found on the surface or within the placental labyrinth, and those injected into the amniotic cavity were found on the surface or within the embryo. Following microinjection at E7.5, survival one week after birth was 60% (26/43) when the amniotic cavity was the target and 66% (19/29) when the target was the ectoplacental cone region. The survival rate was similar in sham experiments, 54% (33/61), for which procedures were identical but no microinjection was performed, suggesting that surgery and manipulation of the uterus were the main causes of embryonic death. CONCLUSION: Ultrasound-guided microinjection into the ectoplacental cone region at E6.5 or E7.5 and the amniotic cavity at E7.5 was achieved with a 7 day postnatal survival of ≥60%. Target accuracy of these sites and of the exocoelomic cavity at E7.5 was ≥51%. We suggest that this approach may be useful for exploring gene function during early placental and embryonic development

On the simulation of nonlinear bidimensional spiking neuron models

Bidimensional spiking models currently gather a lot of attention for their simplicity and their ability to reproduce various spiking patterns of cortical neurons, and are particularly used for large network simulations. These models describe the dynamics of the membrane potential by a nonlinear differential equation that blows up in finite time, coupled to a second equation for adaptation. Spikes are emitted when the membrane potential blows up or reaches a cutoff value. The precise simulation of the spike times and of the adaptation variable is critical for it governs the spike pattern produced, and is hard to compute accurately because of the exploding nature of the system at the spike times. We thoroughly study the precision of fixed time-step integration schemes for this type of models and demonstrate that these methods produce systematic errors that are unbounded, as the cutoff value is increased, in the evaluation of the two crucial quantities: the spike time and the value of the adaptation variable at this time. Precise evaluation of these quantities therefore involve very small time steps and long simulation times. In order to achieve a fixed absolute precision in a reasonable computational time, we propose here a new algorithm to simulate these systems based on a variable integration step method that either integrates the original ordinary differential equation or the equation of the orbits in the phase plane, and compare this algorithm with fixed time-step Euler scheme and other more accurate simulation algorithms

Cop1 constitutively regulates c-Jun protein stability and functions as a tumor suppressor in mice

Biochemical studies have suggested conflicting roles for the E3 ubiquitin ligase constitutive photomorphogenesis protein 1 (Cop 1; also known as Rfwd2) in tumorigenesis, providing evidence for both the oncoprotein c-Jun and the tumor suppressor p53 as its targets. Here we present what we believe to be the first in vivo investigation of the role of Cop1 in cancer etiology. Using an innovative genetic approach to generate an allelic series of Cop1, we found that Cop1 hypomorphic mice spontaneously developed malignancy at a high frequency in the first year of life and were highly susceptible to radiation-induced lymphomagenesis. Further analysis revealed that c-Jun was a key physiological target for Cop1 and that Cop1 constitutively kept c-Jun at low levels in vivo and thereby modulated c-Jun/AP-1 transcriptional activity. Importantly, Cop1 deficiency stimulated cell proliferation in a c-Jun-dependent manner. Focal deletions of COP1 were observed at significant frequency across several cancer types, and COP1 loss was determined to be one of the mechanisms leading to c-Jun upregulation in human cancer. We therefore conclude that Cop1 is a tumor suppressor that functions, at least in part, by antagonizing c-Jun oncogenic activity. In the absence of evidence for a genetic interaction between Cop1 and p53, our data strongly argue against the use of Cop1-inhibitory drugs for cancer therapy

Simulating the Mammalian Blastocyst - Molecular and Mechanical Interactions Pattern the Embryo

Mammalian embryogenesis is a dynamic process involving gene expression and mechanical forces between proliferating cells. The exact nature of these interactions, which determine the lineage patterning of the trophectoderm and endoderm tissues occurring in a highly regulated manner at precise periods during the embryonic development, is an area of debate. We have developed a computational modeling framework for studying this process, by which the combined effects of mechanical and genetic interactions are analyzed within the context of proliferating cells. At a purely mechanical level, we demonstrate that the perpendicular alignment of the animal-vegetal (a-v) and embryonic-abembryonic (eb-ab) axes is a result of minimizing the total elastic conformational energy of the entire collection of cells, which are constrained by the zona pellucida. The coupling of gene expression with the mechanics of cell movement is important for formation of both the trophectoderm and the endoderm. In studying the formation of the trophectoderm, we contrast and compare quantitatively two hypotheses: (1) The position determines gene expression, and (2) the gene expression determines the position. Our model, which couples gene expression with mechanics, suggests that differential adhesion between different cell types is a critical determinant in the robust endoderm formation. In addition to differential adhesion, two different testable hypotheses emerge when considering endoderm formation: (1) A directional force acts on certain cells and moves them into forming the endoderm layer, which separates the blastocoel and the cells of the inner cell mass (ICM). In this case the blastocoel simply acts as a static boundary. (2) The blastocoel dynamically applies pressure upon the cells in contact with it, such that cell segregation in the presence of differential adhesion leads to the endoderm formation. To our knowledge, this is the first attempt to combine cell-based spatial mechanical simulations with genetic networks to explain mammalian embryogenesis. Such a framework provides the means to test hypotheses in a controlled in silico environment