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http://brainliner.jp

Portal and tools for sharing and editing neurophysiological and behavioral data for brain-machine interface research. Users can search for existing data or login with their Google, Facebook, or Twitter account and upload new data. Their main focus is on supporting brain-machine interface research, so we encourage users to not just provide recordings of brain activity data, but also information about stimuli, etc., so that statistical relationships can be found between stimuli and/or subject behavior and brain activity. The Matlab tools are for writing, reading, and converting Neuroshare files, the common file format. A free, open source desktop tool for editing neurophysiological data for brain-machine interface research is also available: https://github.com/ATR-DNI/BrainLiner Since data formats aren''''t standardized between programs and researchers, data and analysis programs for data cannot be easily shared. Neuroshare was selected as the common file format. Neuroshare can contain several types of neurophysiological data because of its high flexibility, including analog time-series data and neuronal spike timing. Some applications have plug-ins or libraries available that can read Neuroshare format files, thus making Neuroshare somewhat readily usable. Neuroshare can contain several types of neurophysiological data, but there were no easy tools to convert data into the Neuroshare format, so they made and are providing a Neuroshare Converter Library and Simple Converter using the library. In future work they will make and provide many more useful tools for data sharing. Shared experiments include: EMG signal, Takemiya Exp, Reconstruct (Visual image reconstruction from human brain activity using a combination of multi-scale local image decoders), SPIKE data, Speech Imagery Dataset (Single-trial classification of vowel speech imagery using common spatial patterns), Functional Multineuron Calcium Imaging (fMCI), Rock-paper-scissors (The data was obtained from subject while he make finger-form of rock/paper/scissors). They also have a page at https://www.facebook.com/brainliner where you can contact us

Proper citation: BrainLiner (RRID:SCR_004951) Copy   

•   Source: SciCrunch Registry

http://211.73.64.34/NNG

Neuron Navigator (NNG) integrates a 3D neuron image database into an easy-to-use visual interface. Via a flexible and user-friendly interface, NNG is designed to help researchers analyze and observe the connectivity within the neural maze and discover possible pathways. With NNG''s 3D neuron image database, researchers can perform volumetric searches using the location of neural terminals, or the occupation of neuron volumes within the 3D brain space. Also, the presence of the neurons under a combination of spatial restrictions can be shown as well. NNG is a result of a multi-discipline collaboration between neuroscientists and computer scientists, and NNG has now been implemented on a coordinated brain space for the Drosophila (fruit fly) brain. Account is required.

Proper citation: Neuron Navigator (RRID:SCR_005063) Copy   

•   Source: SciCrunch Registry

http://mcb.berkeley.edu/labs/werblin/index.html

The goal of our research is to uncover the neural circuitry that mediates some of the remarkable processing capabilities of the retina. The retina to operates at high spatial and temporal resolution over more than 7 orders of magnitude, to detect the direction of motion, to blank and then recover after saccades, to generate at least a dozen different abstract representations of the visual world. How is all of this, and much more, possible in this tissuepaper-thin array of neurons? Videos and images describing this include: * The retinal hypercircuit. * How the Retina Works * Take a Tour through the Retina * Cartoon of the retina showing layering of neurons * Directional Selectivity * Feedback and Crossover inhibition * Multiple Representations of the Visual Scene ** Pseudo Array Recording Methods ** Multiple Representations of the Visual World ** Regions of Frequency Space * Regions of space/time frequency * Space-time rasters for ON and OFF cells * Patching a neuron in a retinal slice * Targeting Retinal Neuron Subregions with Arficial Rhodopsins

Proper citation: Werblin Lab (RRID:SCR_005251) Copy   

•   Source: SciCrunch Registry

http://neurolex.org/wiki/Main_Page

A freely editable semantic wiki for community-based curation of the terms used in Neuroscience. Entries are curated and eventually incorporated into the formal NIFSTD ontology. NeuroLex also includes a Resource branch for community members to freely add neuroscience relevant resources that do not become part of NIFSTD ontology but rather make up the NIF Registry. As part of the NIF, we provide a simple search interface to many different sources of neuroscience information and data. To make this search more effective, we are constructing ontologies to help organize neuroscience concepts into category hierarchies, e.g., neuron is a cell. These categories provide the means to perform more effective searches and also to organize and understand the information that is returned. But an important adjunct to this activity is to clearly define all of the terms that we use to describe our data, e.g., anatomical terms, techniques, organism names. Because wikis provide an easy interface for communities to contribute their knowledge, we started the NeuroLex.

Proper citation: NeuroLex (RRID:SCR_005402) Copy   

•   Source: SciCrunch Registry

http://www.neuroconstruct.org/

Software for simulating complex networks of biologically realistic neurons, i.e. models incorporating dendritic morphologies and realistic cell membrane conductance, implemented in Java and generates script files for the NEURON and GENESIS simulators, with support for other simulation platforms (including PSICS and PyNN) in development. neuroConstruct is being developed in the Silver Lab in the Department of Neuroscience, Physiology and Pharmacology at UCL and uses the latest NeuroML specifications, including MorphML, ChannelML and NetworkML. Some of the key features of neuroConstruct are: Creation of networks of biologically realistic neurons, positioned in 3D space. Complex connectivity patterns between cell groups can be specified for the networks. Can import morphology files in GENESIS, NEURON, Neurolucida, SWC and MorphML format for inclusion in network models. Simulations can be run on the NEURON or GENESIS platforms. Cellular processes (synapses/channel mechanisms) can be imported from native script files or created in ChannelML. Recording of simulation data generated by the simulation and visualization/analysis of data. Stored simulation runs can be viewed and managed through the Simulation Browser interface.

Proper citation: neuroConstruct (RRID:SCR_007197) Copy   

•   Source: SciCrunch Registry

http://www.buzsakilab.com/

Lab interested in understanding how neuronal circuitries of the brain support its cognitive capacities. Its goal is to provide rational, mechanistic explanations of cognitive functions at a descriptive level. In the lab''s view, the most promising area of cognitive faculties for scientific inquiry is memory, since it is a well-circumscribed term, can be studied in animals and substantial knowledge has accumulated on the molecular mechanisms of synaptic plasticity. Available software: * NeuroScope: NeuroScope can display local field potentials (EEG), neuronal spikes, behavioral events, as well as the position of the animal in the environment. It also features limited editing capabilities. * Klusters: Klusters is a powerful and easy-to-use cluster cutting application designed to help neurophysiologists sort action potentials from multiple neurons on groups of electrodes (e.g., tetrodes or multisite silicon probes). * KlustaKwik: KlustaKwik is a program for automatic cluster analysis, specifically designed to run fast on large data sets. * MATLAB m-files: A selection of MATLAB files developed in the lab.

Proper citation: Buzsaki Lab (RRID:SCR_008020) Copy   

•   Source: SciCrunch Registry

http://www.neuroscience.cam.ac.uk/

This portal provides information about the neuroscience department at the University of Cambridge. Cambridge has a strong tradition in neuroscience having been host to the first analyses of neural signaling in the 1930s, determined the mechanisms of neuronal firing in the 1950s, and heralded some of the early theoretical approaches to the functions of neural circuitry in the 1960s. Neuroscience continues to grow at Cambridge, with an impressive record of achievement in multidisciplinary research.

Proper citation: Cambridge Neuroscience Department (RRID:SCR_008649) Copy   

•   Source: SciCrunch Registry

http://www.sfn.org/index.aspx?pagename=brainfacts

Brain Facts is a 74-page primer on the brain and nervous system, published by SfN. Designed for a lay audience as an introduction to neuroscience, Brain Facts is also a valuable educational resource used by high school teachers and students who participate in Brain Awareness Week. The 2008 edition updates all sections and includes new information on brain development, learning and memory, language, neurological and psychiatric illnesses, potential therapies, and more. Download the full book (PDF) or download individual sections. All downloads are PDFs. Educators, request a copy of the Brain Facts book (paperback or CD) - contact BAW@SfN.org.

Proper citation: Brain Facts (RRID:SCR_008788) Copy   

•   Source: SciCrunch Registry

http://eyewire.org/

An online game for mapping neuronal connections in the retina. The site provides microscopic retinal images and uses crowdsourcing to make sense of the images. EyeWire is where the general public can help make discoveries about the neural structure of the retina. The challenge is to map the neural connections of the retina by analyzing images that were acquired using serial electron microscopy at the Max Planck Institute for Medical Research in Heidelberg, Germany. A retinal volume of size 350��300��60 micrometer cubed was imaged, amounting to about one terabyte of data. Retinal Connectome * Game 1: Reconstructing Neurons * Game 2: Identifying Synapses Eyewire incorporates computational technologies developed by the laboratory of Prof. Sebastian Seung at MIT.

Proper citation: EyeWire (RRID:SCR_008816) Copy   

•   Source: SciCrunch Registry

http://www.readout.info

Matlab toolbox that makes it easy to apply decoding analyses to neural data. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and examples are given on how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations.

Proper citation: Neural Decoding Toolbox (RRID:SCR_009012) Copy   

•   Source: SciCrunch Registry

http://www.centropiaggio.unipi.it/software

Software to handle and process large numbers of optical microscopy image files of neurons in culture or slices in order to automatically run batch routines, store data and apply multivariate classification and feature extraction using 3-way principal component analysis (PCA). This freeware for semi automated quantitative and dynamic analysis of neuron morphometry incorporates the most important microstructural quantification methods, such as fractal and sholl analysis with statistical and classification tools to provide an integrated image processing environment which enables fast and easy feature identification. It includes: * Friendly interactive graphical user interface * Image pre-processing * Morphological analysis * Topological analysis * Cell counting * 3-way PCA analysis (also available as an ImageJ plugin) * Plot of variables Sequential images of labeled or unlabelled neurons or tissue slices can be uploaded batch-wise in order to create a 3 axis (time, image coordinate) data base and a datamatrix of variables for 3-way Principal Component Analysis*.

Proper citation: NEuronMOrphological analysis tool (RRID:SCR_006304) Copy   

•   Source: SciCrunch Registry

http://genesis-sim.org/

General purpose simulation platform developed to support the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, simulations of large networks, and systems-level models. As such, GENESIS, and its version for parallel and networked computers (PGENESIS) was the first broad scale modeling system in computational biology to encourage modelers to develop and share model features and components. User contributed GENESIS models and simulations are available. You may to contribute a model or simulation. Educational tutorials for instruction in both neurobiology and computational methods have been developed. These tutorials and GENESIS are now being widely used in graduate and undergraduate instruction. These uses include full semester courses in computational neuroscience or neural modeling, short intensive courses or workshops, an option for a course project, and short units on computational neuroscience within courses on artificial neural nets. They also have a repository of user-contributed tutorials and materials for use in neuroscience education. If you have course descriptions, syllabi, exercises, tutorials, or short HOWTO documents, please upload them to Education.

Proper citation: General Neural Simulation System (RRID:SCR_006316) Copy   

•   Source: SciCrunch Registry

http://www.agedbrainsysbio.eu/

Consortium focused on identifying the foundational pathways responsible for the aging of the brain, with a focus on Late Onset Alzheimer's disease. They aim to identify the interactions through which the aging phenotype develops in normal and in disease conditions; modeling novel pathways and their evolutionary properties to design experiments that identify druggable targets. As early steps of neurodegenerative disorders are expected to impact synapse function the project will focus in particular on pre- or postsynaptic protein networks. The concept is to identify subsets of pathways with two unique druggable hallmarks, the validation of interactions occurring locally in subregions of neurons and a human and/or primate accelerated evolutionary signature. The consortium will do this through six approaches: * identification of interacting protein networks from recent Late-Onset Alzheimer Disease-Genome Wide Association Studies (LOAD-GWAS) data, * experimental validation of interconnected networks working in subregion of a neuron (such as dendrites and dendritic spines), * inclusion of these experimentally validated networks in larger networks obtained from available databases to extend possible protein interactions, * identification of human and/or primate positive selection either in coding or in regulatory gene sequences, * manipulation of these human and/or primate accelerated evolutionary interacting proteins in human neurons derived from induced Pluripotent Stem Cells (iPSCs) * modeling predictions in drosophila and novel mouse transgenic models * validation of new druggable targets and markers as a proof-of-concept towards the prevention and cure of aging cognitive defects. The scientists will share results and know-how on Late-Onset Alzheimer Disease-Genome Wide Association Studies (LOAD-GWAS) gene discovery, comparative functional genomics in mouse and drosophila models, in mouse transgenic approaches, research on human induced pluripotent stem cells (hiPSC) and their differentiation in vitro and modeling pathways with emphasis on comparative and evolutionary aspects. The four European small to medium size enterprises (SMEs) involved will bring their complementary expertise and will ensure translation of project results to clinical application.

Proper citation: AgedBrainSYSBIO (RRID:SCR_003825) Copy   

•   Source: SciCrunch Registry

http://platform.invbrain.neuroinf.jp/

Database of information on nervous systems and behavior of various species of invertebrates and a large body of ancillary material to promote the use of invertebrate systems in research and education and facilitate information transfer to engineers that are looking for mechanisms that may be useful to solve a wide range of technological problems. The database is linked to explanations of the contents to allow users to familiarize themselves with the data and the context in which they were obtained. The platform has four entrance points tailored to different target user groups. The first entrance point is designed for users that are interested in using invertebrates for research purposes, in particular in the field of neuroscience, to assist them in initiating research projects. This includes databases of sensory systems, brains, and behavior of invertebrates, especially insects. The databases contain sensory organ structure and function, photographs and movies documenting insect behavior, data acquisition equipment and other instrumentation, software, material for eduction, and bibliography. A second entrance point is available for those that are concerned with implementations of design principles of invertebrate nervous systems and behavior in industrial applications. The third portal is destined for providing quick access for instructors that intend to use invertebrates for educational purposes and the remaining entrance point facilitates obtaining general comparative information on sensory and central nervous systems and behavior of invertebrates.

Proper citation: Invertebrate Brain Platform (RRID:SCR_006764) Copy   

•   Source: SciCrunch Registry

http://neuronvisio.org/

A Graphical User Interface for NEURON simulator environment with 3D capabilities. Neuronvisio makes easy to select and investigate sections'''' properties and it offers easy integration with matplotlib for plotting the results. The geometry can be saved using NeuroML and the computational results in a customized and extensible HDF5 format; the results can then be reload in the software and analyzed in a later stage, without re-running the simulation. Featuring 3D visualization of the model with the possibility to change it runtime; creation of vectors to record any variables present in the section; pylab integration to plot directly the result of the simulation; exploration of the timecourse of any variable among time using a color coded scale; saving the results simulation for later analysis; automatic download and running of models in ModelDB.

Proper citation: NeuronVisio (RRID:SCR_006839) Copy   

•   Source: SciCrunch Registry

http://www.utsa.edu/claibornelab/

The long-term goals of my research are to understand the relationship between neuronal structure and function, and to elucidate the factors that affect neuronal morphology and function over the lifespan of the mammal. Currently we are examining 1) the effects of synaptic activity on neuronal development; 2) the effects of estrogen on neuronal morphology and on learning and memory; and, 3) the effects of aging on neuronal structure and function. We have focused our efforts on single neurons in the hippocampal formation, a region that is critical for certain forms of learning and memory in rodents and humans. From the portal, you may click on a cell in your region of interest to see the complete database of cells from that region. You may also explore the Neuron Database: * Comparative Electrotonic Analysis of Three Classes of Rat Hippocampal Neurons. (Raw data available) * Quantitative, three-dimensional analysis of granule cell dendrites in the rat dentate gyrus. * Dendritic Growth and Regression in Rat Dentate Granule Cells During Late Postnatal Development.(Raw data available) * A light and electron microscopic analysis of the mossy fibers of the rat dentate gyrus.

Proper citation: University of Texas at San Antonio Laboratory of Professor Brenda Claiborne (RRID:SCR_008064) Copy   

•   Source: SciCrunch Registry

http://www.strout.net/conical/

CONICAL is a C++ class library for building simulations common in computational neuroscience. Currently its focus is on compartmental modeling, with capabilities similar to GENESIS and NEURON. Future classes may support reaction-diffusion kinetics and more. A key feature of CONICAL is its cross-platform compatibility; it has been fully co-developed and tested under Unix, DOS, and Mac OS. Any C++ compiler which adheres to the emerging ANSI standard should be able to compile the CONICAL classes without modification. It is intended to encourage the rapid development of simulator software, especially on non-Unix systems where such software is sorely lacking. The present focus of the CONICAL library of C++ classes is compartmental modeling. A model neuron is built out of compartments, usually with a cylindrical shape. When small enough, these open-ended cylinders can approximate nearly any geometry, just as the stack of cylinders approximates a cone in the logo above. While any compartment has passive electrical properties (like a simple resistor-capacitor circuit), more interesting properties require the use of active ion channels whose conductance varies as a function of the time or membrane voltage. A standard Hodgkin-Huxley ion channel is included as one of the built-in CONICAL object types. Most of the voltage-gated ion channels in the literature can be directly implemented merely by setting the parameters of this class. For extensibility, this class is derived from several layers of more general classes. Connections between neurons can be implemented in several ways. For a gap junction (i.e., simple electrical connection), a passive current (or pair of currents, one in each direction) can be used. Synapses are more complex objects, but used in a similar fashion. The Alpha-function synapse is a very popular model of synaptic transmission, and is a basic CONICAL class. More complex (and realistic) synapses can be built using the Markov-model synapse. (A Markov model can be used on its own for other purposes as well.) In addition to classes directly related to neural modeling, CONICAL contains several other useful object types. These include a current injector, and a column-oriented output stream for storing data in table form.

Proper citation: Conical: The Computational Neuroscience Class Library (RRID:SCR_008318) Copy   

•   Source: SciCrunch Registry

http://www.med.nus.edu.sg/ant/histonet/txt/menu/nervmenu.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 18, 2013. 15 annotated electron micrographs of different parts of the nervous system. Different nerve tissues are depicted.

Proper citation: Nerve Tissue (RRID:SCR_008219) Copy   

•   Source: SciCrunch Registry

http://www.math.uh.edu/~mpapadak/centerline/

An application for the automatic segmentation and tracing of three-dimensional neuronal images.

Proper citation: centerline (RRID:SCR_002961) Copy   

•   Source: SciCrunch Registry

http://www.neurogems.org/neosim/

Simulation software that includes a parallel discrete event simulation kernel for running models of spiking neurons on a cluster of workstations. Models are specified using NeuroML, and visualized using Java2D. Simulation components are distributed across a parallel machine or network and communicate using timestamped events. The successor NEOSIM2 project under the NeuroGems umbrella at Edinburgh University (http://www.neurogems.org) continues to distribute the software, http://www.neurogems.org/neosim2/ The NEOSIM project includes: * a parallel discrete event simulation kernel for running models of spiking neural networks on clusters of machines. * a modules kit for extending the behavior of neurons and connectivity patterns. * a user interface for building and running simulations. OS: Linux, MS-Windows

Proper citation: Neural Open Simulation (RRID:SCR_002916) Copy   

•   Source: SciCrunch Registry