The Neuron Navigator: Exploring the information pathway through the neural maze

Lin, Chen; Tsai, Kuen-Long; Wang, Sheng-Chuan; Hsieh, Chang-Huain; Chang, Hao‐Ming; Chiang, Ann‐Shyn

doi:10.1109/pacificvis.2011.5742370

Cited by 22 publications

(15 citation statements)

References 23 publications

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“…In comparison to other scientific visualisation tools for neural networks such as BrainGazer (Bruckner et al, 2009) and Neuron Navigator (NNG) (Lin et al, 2011), our solution differs in that the user can naturally navigate through the 3D space by simply walking and gesturing instead of using mouse and keyboard shortcuts.…”

Section: Dynamic and Immersive Visualisation Of Neucube Modelsmentioning

confidence: 99%

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

et al. 2016

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The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.

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Section: Dynamic and Immersive Visualisation Of Neucube Modelsmentioning

confidence: 99%

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

et al. 2016

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“…Hadwiger et al [24] present a volume visualization system for large EM volumes, which was later extended to segmented EM volumes [25]. Several systems, such as Neuron Navigator [31] and Connectome Explorer [15] support interactive or visual queries to further explore these typically very large data sets.…”

Section: Related Workmentioning

confidence: 99%

Scalable Interactive Visualization for Connectomics

et al. 2017

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Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput-from storage to display-in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics.

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“…BrainGazer (Bruckner et al, 2009), BrainBrowser (Sherif et al, 2015), BrainNet (Xia et al, 2013) and Neuron Navigator (NNG) (Lin et al, 2011) are examples of the category of tools for analysis. They are designed for and used in context with models of realistic biological neural networks such as the human connectome or the brain of the fruitfly Drosophila melanogaster (Armstrong and van Hemert, 2009).…”

Section: Related Workmentioning

confidence: 99%

Immersive visualisation of 3-dimensional spiking neural networks

Marks

2016

Evolving Systems

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Recent development in artificial neural networks has led to an increase in performance, but also in complexity and size. This poses a significant challenge for the exploration and analysis of the spatial structure and temporal behaviour of such networks. Several projects for the 3D visualisation of neural networks exist, but they focus largely on the exploration of the spatial structure alone, and are using standard 2D screens as output and mouse and keyboard as input devices. In this article, we present NeuVis, a framework for an intuitive and immersive 3D visualisation of spiking neural networks in virtual reality, allowing for a larger variety of input and output devices. We apply NeuVis to NeuCube, a 3-dimensional spiking neural network learning framework, significantly improving the user's abilities to explore, analyse, and also debug the network. Finally, we discuss further venues of development and alternative render methods that are currently under development and will increase the visual accuracy and realism of the visualisation, as well as further extending its analysis and exploration capabilities.

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The Neuron Navigator: Exploring the information pathway through the neural maze

Cited by 22 publications

References 23 publications

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications

Scalable Interactive Visualization for Connectomics

Immersive visualisation of 3-dimensional spiking neural networks

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