In stakeholder participation workshops, digital and hard-copy maps, alongside other representation formats in 2D and 3D, are used extensively to support communication, spatial evaluation and interactive decision making processes. In this paper, we present a novel tool to enhance traditional map-based workshop activities using augmented reality. Augmented reality technology is gaining momentum as a tool for visualising environmental design choices in planning and design, and is used in a range of applications including stakeholder participation, design evaluation and flood risk communication. We demonstrate interactive and collaborative 3D cartographic visualisations which enable real-time multi-user exercises in landscape intervention design and flood visualisation.

The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works. To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

Summary: The Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.

The importance of simulator-independent high-level model-description languages for model building, sharing, and integration in computational neuroscience has been well established. Simulator independence is essential to ensure that complex models are both testable and reproducible. However the adoption of standardisation has been hampered by the current difficulty of developing and visualising Layered XML models, due to a lack of intuitive model creation tools. Concurrently, the development of increasingly complex and comprehensive simulations, such as whole-brain emulation, require a shift to massively parallel programming paradigms. GPU utilization has proven to be a powerful tool for reducing execution times of the large-scale models required for rapid hypothesis generation and testing. Problematically, while parallel programming is a powerful tool, it is notoriously difficult and time consuming for non-experts to develop efficient, portable and testable code. This requirement for efficient GPU-level programming presents a further barrier to entry for open-science, due to both the increased financial burden of GPU infrastructure, and the specialized knowledge needed for proficiency specific GPU programming paradigms. This in turn negatively impacts the progress model sharing and integration. Here we describe a complete GUI to GPU tool chain based on SpineML (Richmond et al, 2014) , a LEMS derived layered neural modeling language (Cannon et al. 2014), and the associated graphical user interface, SpineCreator. We demonstrate easy development of large scale neural networks, with support for both CPU and GPU code generation. We prototype both local and remote code execution, encouraging large-scale, parallel-execution of standardised model specifications, with seamless model sharing. We demonstrate the capability of running these models both on local hardware, and with easily set up cloud computing services, enabling GPU utilization, without requiring the considerable time and financial investments associated with GPU development and maintenance. As an important model for neurological investigation, with a large genetic tool-base, we focus here on the Drosophila model organism, and utilise the Neurokernel GPU based simulation platform (Givon et al. (2016)), aimed at facilitating easy model integration through modularly defined drosophila Local Processing Units (LPUs). To enable model translation from XML to GPU code we developed the libSpineML python library as a pythonic interface to the model description, to facilitate programmatic model creation and translation into executable Neurokernel modules. Using this programmatic SpineML interface, we developed the toolchain to transform arbitrary neuron components into CUDA kernels (Goodman et al. (2010)), which can run in massively parallel network simulations natively in the Neurokernel GPU framework. Building on this modular pipeline, we show how locally created high level network specifications can be compiled down to efficient CUDA code to be executed locally, or remotely. We detail a novel web-interface and framework for GPU-cloud computing access, further reducing the barrier to entry to massively parallel neural simulations. What results is a flexible, powerful, and open-standard based toolchain which empowers intuitive graphical neuroscientific simulation design, with seamless dynamic GPU code generation. This combination of large scale massively-parallel simulations, with graphical creation tools and remote execution through cloud access, will simultaneously reduced the barrier to entry for GPU-enabled simulations, and significantly improve the development process for the sharable, testable and reproducible models required in successful open science initiatives.