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http://dynamicbrain.neuroinf.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19. 2022. Platform to promote studies on dynamic principles of brain functions through unifying experimental and computational approaches in cellular, local circuit, global network and behavioral levels. Provides services such as data sets, popular research findings and articles and current developments in field. This site has been archived since FY2019 and is no longer updated.

Proper citation: Dynamic Brain Platform (RRID:SCR_001754) Copy   

•   Source: SciCrunch Registry

http://neuralprediction.berkeley.edu/

The aim of the Neural Prediction Challenge is to accelerate the development of predictive models and to provide computational neuroscientists an opportunity to test their models objectively. The challenge is really quite simple: you will be given some (visual and/or auditory) stimuli and corresponding neural responses, and you must try to predict responses to other stimuli. Each data set will be divided in to two subsets: a fit set (90% of the data) that includes both the stimuli and the corresponding neuronal responses; and a validation set (10% of the data) that includes only stimuli (no responses). Your job is to use the fit set to fit your model and then to generate predicted responses based on the stimuli provided in the validation set. Once you have the predictions you should return them to us. We will compare your predicted responses to the responses actually observed in the validation set. Current data consist of recordings from visual and auditory neurons during naturalistic stimulation. Data are provided in simple ascii files that are easily readable in Matlab (or by any other modern programming language). Details on data formatting are provided with each data set. Predictions will be evaluated continuously as they are received and results will be posted in aggregate form. Individuals' names, prediction scores and models will not be posted without prior permission (though we may contact participants directly, see official rules). Please note that this is an academic research project, it is not a traditional contest. There is no real ending date, and there is nothing to win. Sponsors: This project is supported by the NIH Human Brain Project.

Proper citation: University of California at Berkeley: The Neural Prediction Challenge (RRID:SCR_001920) Copy   

•   Source: SciCrunch Registry

http://flybrain.stanford.edu/

Project content including raw image data, neuronal tracings, image registration tools and analysis scripts covering three manuscripts: Comprehensive Maps of DrosophilaHigher Olfactory Centres : Spatially Segregated Fruit and Pheromone Representation which uses single cell labeling and image registration to describe the organization of the higher olfactory centers of Drosophila; Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe which uses single cell labeling to describe the organization of the antennal lobe local interneurons; and Sexual Dimorphism in the Fly Brain which uses clonal analysis and image registration to identify a large number of sex differences in the brain and VNC of Drosophila. Data * Raw Data of Reference Brain (pic, amira) (both seed and average) * Label field of LH and MB calyx and surfaces for these structures * Label field of neuropil of Reference Brain * Traces (before and after registration). Neurolucida, SWC and AmiraMesh lineset. * MB and LH Density Data for different classes of neuron. In R format and as separate amira files. * Registration files for all brains used in the study * MBLH confocal images for all brains actually used in the study (Biorad pic format) * Sample confocal images for antennal lobe of every PN class * Confocal stacks of GABA stained ventral PNs Programs * ImageJ plugins (Biorad reader /writer/Amira reader/writer/IGS raw Reader) * Binary of registration, warp and gregxform (macosx only, others on request) * Simple GUI for registration tools (macosx only at present) * R analysis/visualization functions * Amira Script to show examples of neuronal classes The website is a collaboration between the labs of Greg Jefferis and Liqun Luo and has been built by Chris Potter and Greg Jefferis. The core Image Registration tools were created by Torsten Rohlfing and Calvin Maurer.

Proper citation: Flybrain at Stanford (RRID:SCR_001877) Copy   

•   Source: SciCrunch Registry

http://neuromorpho.org/index.jsp

Centrally curated inventory of digitally reconstructed neurons associated with peer-reviewed publications that contains some of the most complete axonal arborizations digitally available in the community. Each neuron is represented by a unique identifier, general information (metadata), the original and standardized ASCII files of the digital morphological reconstruction, and a set of morphometric features. It contains contributions from over 100 laboratories worldwide and is continuously updated as new morphological reconstructions are collected, published, and shared. Users may browse by species, brain region, cell type or lab name. Users can also download morphological reconstructions for research and analysis. Deposition and distribution of reconstruction files ultimately prevents data loss. Centralized curation and annotation aims at minimizing the effort required by data owners while ensuring a unified format. It also provides a one-stop entry point for all available reconstructions, thus maximizing data visibility and impact.

Proper citation: NeuroMorpho.Org (RRID:SCR_002145) Copy   

•   Source: SciCrunch Registry

http://connectomes.org/

Project mapping whole mouse brain connectivity using serial block face scanning electron microscopy (SBF-SEM) with a specially-designed whole-brain microtome (WBM). With any luck, the whole mouse brain will be mapped ultrastructurally in the near term, which will then open the door to more serious problems; reliable automated segmentation and circuit reconstruction. These will undoubtedly require advances in machine learning methods and their application. Connectomics Software and a Multiresolution Image Viewer (MIV) is also available.

Proper citation: Connectomes.org (RRID:SCR_002243) Copy   

•   Source: SciCrunch Registry

http://www.janelia.org/team-project/fly-em

A project producing datasets, software, and algorithms that is developing the technology to produce connectomes at the electron microscopic level of behaviorally-relevant neural circuits as well as the entire Drosophila nervous system. This technology will enable them to create a map of every neuron and synapse in the Drosophila nervous system, using novel approaches to electron microscopy (EM) as the foundation. In the same way that the fly genome paved the way for larger projects, including sequencing the human genome, Fly EM may ultimately contribute to our understanding of the human brain by establishing a fly "connectome" a map that shows how all neurons in the fly brain are connected to each other. They began their entry into EM reconstruction with the fly's adult visual system, where much is known about cell types from previous EM and histological studies, as well as ongoing studies in the Fly Light Project. In addition to establishing and publishing a fly connectome, Fly EM will make technology and methodology available that is needed to perform large-scale EM reconstructions. Fly EM will generally pursue an open policy with their datasets, software, and algorithms after relevant publications. When an EM reconstruction is published, the derived connectome and reconstructed neuronal skeletons will be made available online. The raw data and annotatations will be made available upon request as logistics dictate. To encourage further collaboration and scientific discovery, a small fraction of their raw data and corresponding segmentation will be made available independent of publication. Their goal is to enable others who wish to approach the many algorithmic challenges, but who do not have access to an EM facility, to have the data they need to support methods development, as well as their results to use as a benchmark. Fly EM emphasizes publication of supporting techniques and software approaches before major EM reconstruction releases to encourage rapid feedback from the community and adoption of their strategies. FlyEM maintains much of its software in the open-source repository GitHub:http://janelia-flyem.github.com. They will provide information on official release versions of these packages on git-hub when it reaches reasonable maturity.

Proper citation: Fly EM (RRID:SCR_002242) Copy   

•   Source: SciCrunch Registry

http://www.theearlab.org

A computationally oriented experimental laboratory interested in the encoding of auditory information in the cerebral cortex and brainstem, and in the mechanisms of tinnitus and the effect of various drugs (Lidocaine, steroids, anti-oxidants) in relieving noise trauma induced tinnitus. The ferret (Mustela putorius) and the rat serve as their system model. Through chronic implants, they obtain electrophysiological data from awake behaving animals in order to investigate the response properties and functional organization of the auditory system, both in health and after noise trauma that induces tinnitus in rats. Projects: * Response Modulation to Ongoing Broadband Sounds in Primary Auditory Cortex * Neuronal Response Characteristics in the Inferior Colliculus of the Awake Ferret and Rat * Spectro-Temporal Representation of Feature Onsets in Primary Auditory Cortex * Targeting the changes in inferior colliculus induced by tinnitus

Proper citation: Ear Lab (RRID:SCR_002531) Copy   

•   Source: SciCrunch Registry

http://incfnrcihrd.appspot.com/signin

Repository of neuron types characterized by machine-readable part-relation-value triple-based neuron properties. A Curator Interface facilitates the direct knowledge transfer of information from the participating neuroscientist for entry into the Neuron Registry.

Proper citation: Neuron Registry Curator Interface (RRID:SCR_000094) Copy   

•   Source: SciCrunch Registry

http://neurospy.org

neurospy is a free software for functional imaging of fast neuronal activity. neurospy is a modular cross-platform application framework written in Java for the NetBeans Platform. At this time it runs on Windows XP-based LeCroy oscilloscopes and drives acousto-optic scanners via USB using the Analog Devices 9959 Direct Digital Synthesis chip. This combination makes one of the most powerful systems for scanning microscopy available today at any price. neurospy is very easy to port to other kinds of acquisition and scanning hardware.

Proper citation: neurospy (RRID:SCR_007016) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/neuronpm/

Client/server application that makes parameter maps for neural simulations written in NEURON.

Proper citation: neuronPM (RRID:SCR_014768) Copy   

•   Source: SciCrunch Registry

http://mariannebezaire.com/simtracker/

MATLAB-based tool for use with NEURON network models. It supports the modeler with model design, organization, execution (including on supercomputers), analysis, and reproducibility.

Proper citation: SimTracker (RRID:SCR_014735) Copy   

•   Source: SciCrunch Registry

https://www.mbfbioscience.com/neurolucida-explorer

Companion analytical software for Neurolucida and Neurolucida 360, designed to perform extensive morphometric analysis on neuron reconstructions, serial section reconstructions, and brain maps.

Proper citation: Neurolucida Explorer (RRID:SCR_017348) Copy   

•   Source: SciCrunch Registry

http://backyardbrains.com/

A commercial company that sells affordable brain recording kits geared for neuroscience education purposes. Their products are meant to mimic electroencephalograms, a tool that measures the electrical activity of neurons.

Proper citation: Backyard Brains (RRID:SCR_000704) Copy   

•   Source: SciCrunch Registry

http://www.wholebraincatalog.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. An open source, downloadable, 3d atlas of the mouse brain and its cellular constituents that allows multi-scale data to be visualized in a seamless way, similar to Google earth. Data within the Catalog is marked up with annotations and can link out to additional data sources via a semantic framework. This next generation open environment has been developed to connect members of the neuroscience community to facilitate solutions for today's intractable challenges in brain research through cooperation and crowd sourcing. The client-server platform provides rich 3-D views for researchers to zoom in, out, and around structures deep in a multi-scale spatial framework of the mouse brain. An open-source, 3-D graphics engine used in graphics-intensive computer gaming generates high-resolution visualizations that bring data to life through biological simulations and animations. Within the Catalog, researchers can view and contribute a wide range of data including: * 3D meshes of subcellular scenes or brain region territories * Large 2D image datasets from both electron and light level microscopy * NeuroML and Neurolucida neuronal reconstructions * Protein Database molecular structures Users of the Whole Brain Catalog can: * Fit data of any scale into the international standard atlas coordinate system for spatial brain mapping, the Waxholm Space. * View brain slices, neurons and their animation, neuropil reconstructions, and molecules in appropriate locations * View data up close and at a high resolution * View their own data in the Whole Brain Catalog environment * View data within a semantic environment supported by vocabularies from the Neuroscience Information Framework (NIF) at http://www.neuinfo.org. * Contribute code and connect personal tools to the environment * Make new connections with related research and researchers 5 Easy Ways to Explore: * Explore the datasets across multiple scales. * View data closely at high resolution. * Observe accurately simulated neurons. * Readily search for content. * Contribute your own research.

Proper citation: Whole Brain Catalog (RRID:SCR_007011) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/index3.html

Multidisciplinary research team devoted to the study of basic neuroscience with a specific interest in the description and generation of dendritic morphology, and in its effect on neuronal electrophysiology. In the long term, they seek to create large-scale, anatomically plausible neural networks to model entire portions of a mammalian brain (such as a hippocampal slice, or a cortical column). Achievements by the CNG include the development of software for the quantitative analysis of dendritic morphology, the implementation of computational models to simulate neuronal structure, and the synthesis of anatomically accurate, large scale neuronal assemblies in virtual reality. Based on biologically plausible rules and biophysical determinants, they have designed stochastic models that can generate realistic virtual neurons. Quantitative morphological analysis indicates that virtual neurons are statistically compatible with the real data that the model parameters are measured from. Virtual neurons can be generated within an appropriate anatomical context if a system level description of the surrounding tissue is included in the model. In order to simulate anatomically realistic neural networks, axons must be grown as well as dendrites. They have developed a navigation strategy for virtual axons in a voxel substrate.

Proper citation: Computational Neuroanatomy Group (RRID:SCR_007150) Copy   

•   Source: SciCrunch Registry

http://senselab.med.yale.edu/modeldb/

Curated database of published models so that they can be openly accessed, downloaded, and tested to support computational neuroscience. Provides accessible location for storing and efficiently retrieving computational neuroscience models.Coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. The model source code has to be available from publicly accessible online repository or WWW site. Original source code is used to generate simulation results from which authors derived their published insights and conclusions.

Proper citation: ModelDB (RRID:SCR_007271) Copy   

•   Source: SciCrunch Registry

http://flybrain.neurobio.arizona.edu/

An interactive database of the Drosophila melanogaster nervous system. It is used by the drosophila neuroscience community and by other researchers studying arthropod brain structure. Flybrain contains neuroanatomical peer reviewed descriptions of the central and peripheral nervous system of Drosophila melanogaster. It also contains an introductory hypertext tour guide to the basic structure of the nervous system, as well as more specific information concerning different anatomical structures, developmental stages, and visualization techniques for the Drosophila nervous system. Additionally, The site contains schematic representations, a 3D project, immunocytology stains, a library of golgi impregnations, and enhancer-trap images.

Proper citation: MIRROR: FlyBrain, An Online Atlas and Database of the Drosophila Nervous System (RRID:SCR_007661) Copy   

•   Source: SciCrunch Registry

https://www.bi.mpg.de/borst

Merger of the Max Planck Institute of Neurobiology and the Max Planck Institute of Ornithology and has been renamed to Circuits - Computation – Models. Department devoted to the study of how the brain computes to understand neural information processing at the level of individual neurons and small neural circuits.

Proper citation: Max Planck Institute for Biological Intelligence Circuits - Computation – Models (RRID:SCR_008048) Copy   

•   Source: SciCrunch Registry

http://bellsouthpwp.net/c/a/capowski//NTSPublic.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A hardware and software package with which a scientist could trace the structure of neurons and other neuroscientific features directly from tissue sections or from a stack of their images into a computer. Then it also could edit, merge, filter, display in 3D, and make realistic plots of the structures. The NTS also includes a substantial statistical package that provided many, now standardized, mathematical and statistical summaries that described each neuron and compared one population to another. Additionally, NTS also provided an embryonic electrotonic modeler that simulates and displayes the electrical functioning of a cell. The NTS uses a special purpose graphics display processor called the VDP3 whose output is presented on a very high resolution CRT. During tracing, the VDP3 presents a variable-diameter cursor and other information directly in the microscope and enables tracing at a high spatial resolution and with measurement of process diameters limited only by the microscope''s optics. Control of tracing is done with a 3D joystick that allows easy control of five input variables: X,Y,Z position, cursor diameter, and a numeric tag. Finally, superb 3D interactive displays of completed cells are provided on the VDP3.

Proper citation: Eutectic NTS (RRID:SCR_008062) Copy   

•   Source: SciCrunch Registry

http://www.neurostruct.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A built-in toolbox for the tracing and analysis of neuroanatomy from nanoscale (high-resolution) imaging. It is a project under ongoing development. The name is originating by merging the words Neuron + reconstruct. The working concept is organized in filters applied successively on the image stack to be processed (pipeline). Currently, the focus of the software is the extraction of detailed neuroanatomical profiles from nanoscale imaging techniques, such as the Serial Block-Face Scanning Electron Microscopy (SBFSEM). The techniques applied, however, may be used to analyze data from various imaging methods and neuronal versatility. The underlying idea of Neurostruct is the use of slim interfaces/filters allowing an efficient use of new libraries and data streaming. The image processing follows in voxel pipelines by using the CUDA programming model and all filters are programmed in a datasize-independent fashion. Thus Neurostruct exploits efficiency and datasize-independence in an optimal way. Neurostruct is based on the following main principles: * Image processing in voxel pipelines using the general purpose graphics processing units (GPGPU) programming model. * Efficient implementation of these interfaces. Programming model and image streaming that guarantees a minimal performance penalty. * Datasize-independent programming model enabling independence from the processed image stack. * Management of the filters and IO data through shell scripts. The executables (filters) are currently managed through shell scripts. The application focuses currently in the tracing of single-biocytin filled cells using SBFSEM imaging. : * Extraction of neuroanatomical profiles: 3D reconstrution and 1D skeletons of the imaged neuronal structure. * Complete tracing: Recognition of the full neuronal structure using envelope techniques, thereby remedying the problem of spines with thin necks of an internal diameter approaching the SBFSEM resolution. * Separation (Coloring) of subcellular structures: Algorithms for the separation of spines from their root dendritic stem. * Evaluation and analysis of the imaged neuroanatomy: Calculation of the dendritic and spine membrane''s surface, spine density and variation, models of dendrites and spines

Proper citation: Neurostruct (RRID:SCR_008861) Copy   

•   Source: SciCrunch Registry