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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://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   

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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   

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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   

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http://code.google.com/p/ontomorphtab/

OntoMorph is a tab plugin for Protege-OWL 3 that allows a user to mark-up portions of a Neurolucida neuron morphology with OWL instances. A user loads a Neurolucida morphology file, either from their hard drive or from an arbitrary URL, into an interface that allows them to zoom, rotate, and translate the morphology. The interface allows them to select points on the morphology to indicate points, segments, or subtrees of the morphology they wish to assign to an OWL instance. After this selection has been made, OntoMorph saves the selection to the currently active OWL instance in the ontology that is currently loaded into Protege. No modifications are made to the Neurolucida file itself. As a result, an association is created between that portion of the morphology and the OWL instance, such that selecting the OWL instance allows retrieval of the portion. Upon retrieval, the morphology portion can be highlighted, so the user can keep track of what pieces each instance refer to.

Proper citation: OntoMorph Tab (RRID:SCR_000443) Copy   

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http://www.blki.hu/~szucs/OS3.html

Orbital Spike is a tool for time series analysis. It contains a wide range of methods to analyze data from point processes such as spike arrival times, heart beats or other behavioral episodes. It is optimized this program for spike trains but it works with other types of data, too. The program can analyze up to 8 channels recorded simultaneously each containing a maximum of 132,000 events (spikes). Assuming an average firing rate of 10 Hz for a neuron, you can then analyze a time series of approximately 3 and half hours long. There are up to 8 panels shown in the Orbital Spike desktop. The panels will contain the kind of data of interest. The graphs are associated with a bunch of parameters like window width, bin size, resolution, delay etc. All these parameters are listed in the parameter box, which appears on the right side of the desktop. It is pretty easy to change the parameters and what is nice, the corresponding graph(s) will be recalculated immediately. You can also use a dialog box to change parameters. There are a lot of functions, statistics, graphs and diagrams available. A few of them are: * Interspike interval sequences * ISI Poincar * maps or return maps Instantaneous firing rate * ISI histograms and probability densities * Joint ISI and MSI probability densitograms * Autocorrelation, crosscorrelation * Spike density functions using kernel estimators * Fourier-amplitude spectrum and spectogram * Symbolic maps, recurrence plots * Phase plots of spike density functions Sponsors: Support for this work came from the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Engineering and Geosciences, under Grants DE-FG03-90ER14138 and DE-FG03-96ER14592; from the Office of Naval Research under Grant N00014-00-1-0181; from the National Science Foundation under Grant PHY0097134; from the National Institutes of Health under Grants R01 NS-40110-01A2 and 1RO1 NS-40110; and from the Army Research Office under Contract DAAD19-01-1-0026. R. D. Pinto was supported by the State of Sao Paulo Research Foundation (FAPESP).

Proper citation: Spike Train Analysis Software by Attila Szucs: Orbital Spike 4 (RRID:SCR_001868) Copy   

•   Source: SciCrunch Registry

http://165.124.81.52/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 08, 2013. A consortium of three facilities whose purpose is to establish, characterize, and distribute novel mutant mouse models with neural and/or behavioral phenotypes, and distribute them to the worldwide research community. Interested scientists are able to obtain information about mouse lines at all three sites in a single unified database. GOALS * Increase genomic and genetic tools for functional gene identification * Provide mice with mutations that alter the nervous system or behavior * Build collaborations between geneticists and neuroscientists The consortium is made up of three mutagenesis and phenotypic screening facilities, focused on identifying alterations in nervous system function and behavior, and established by NIH. They are the Neurogenomics Project at Northwestern University, the Neuroscience Mutagenesis Facility at The Jackson Laboratory, and the Neuromutagenesis Project of the Tennessee Mouse Genome Consortium. The NIH Neurogenomics Project at Northwestern University is directed by Dr. Joseph S. Takahashi, who also acts as the Director of the Neuromice.org consortium. Chemical mutagenesis is used to induce mutations throughout the genome and combined with phenotypic screens to detect mice with mutations. In order to maximize the genomic coverage and recover both dominant and recessive mutations, a dominant G1 screen and a recessive G3 screen are utilized. Phenotypic screens focus on five primary domains: learning and memory, behavioral responses to stress, responses to psychostimulants, circadian rhythmicity, and vision. The Neuroscience Mutagenesis Facility at the Jackson Laboratory is directed by Dr. Wayne N. Frankel. The Neuroscience Mutagenesis Facility is using a three-generation backcross breeding scheme to produce homozygous mutants and will thus recover dominant, semidominant, and recessive mutations. In addition, some mutagenesis will be done in ES cells followed by two generations of breeding. Phenotypic screens focus on identifying mutations affecting: motor function, seizure threshold, hearing, vision, and neurodevelopment. The Neuromutagenesis Project of the Tennessee Mouse Genome Consortium (TMGC) involves researchers throughout the state of Tennessee, under the direction of Dr. Daniel Goldowitz, Ph.D., at the University of Tennessee Health Science Center, Memphis. TMGC also includes researchers at Oak Ridge National Laboratory, Vanderbilt University, Meharry Medical College, University of Tennessee-Knoxville, St. Jude Children's Research Hospital, and the University of Memphis. The Project is using regional mutagenesis, covering regions on chromosomes 10, 14, 15, 19, and X, thus including approximately 15 of the genome in the screened region. Phenotypic screens include: motor and sensory function, learning and memory, neurohistology, aging, alcohol response, abused drug response, visual function, and social behavior. Neuromice.org has stopped taking orders online but mutants are orderable please contact the originating center for availability and pricing details. Live targeted mutant Fragile X model mice are now available for distribution.

Proper citation: neuromice (RRID:SCR_002993) Copy   

•   Source: SciCrunch Registry

http://www.imagescience.org/meijering/software/neuronj/

NeuronJ is an ImageJ plugin to facilitate the tracing and quantification of elongated structures in two-dimensional (2D) images (8-bit gray-scale and indexed color), in particular neurites in fluorescence microscopy images. Sponsors: The development of NeuronJ started while the primary developer ( Dr. Erik Meijering, PhD) was with the Biomedical Imaging Group (collaborating with people from the Laboratory of Cellular Neurobiology) of the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and was finished while Dr. Meijering was with the Biomedical Imaging Group Rotterdam in the Netherlands.

Proper citation: NeuronJ: An ImageJ Plugin for Neurite Tracing and Quantification (RRID:SCR_002074) Copy   

•   Source: SciCrunch Registry

http://ml-neuronbrowser.janelia.org/

Interactive web platform for anyone to explore, search, filter and visualize the single neuron reconstructions.

Proper citation: MouseLight Neuron Browser (RRID:SCR_016669) Copy   

•   Source: SciCrunch Registry

https://gitlab.orc.gmu.edu/kbijari/zebrafish-analysis-protocol

Project for quantitative neuronal morphometry by supervised and unsupervised learning. Includes protocol to quantify and interpret morphological properties of individual neurons reconstructed from microscopic imaging.Includes information about installation of analysis tools and downloading datasets and custom codes.

Proper citation: neuronal reconstruction analysis project (RRID:SCR_021638) Copy   

•   Source: SciCrunch Registry

http://neuroml-db.org

The NeuroML database is a curated relational database that provides for the storage and retrieval of computational neuroscience models expressed in NeuroML, which is an extensible XML-based language for describing complex mathematical models of neurons and neuronal networks. NeuroML models are unique in their modular and multi-scale structure, where subcomponents of models can correspond to neuroscience models. In particular, the NeuroML database allows for efficient searches over the components of models and metadata that are associated with a hierarchical NeuroML model description.

Proper citation: NeuroML Database (RRID:SCR_013705) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/NIF-Ontology/tree/neurons/ttl

An ontology for describing the complex phenotypes of neurons.

Proper citation: Neuron Phenotype Ontology (RRID:SCR_017403) Copy   

•   Source: SciCrunch Registry

http://www.biological-networks.org/p/outliers/

Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011). This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.

Proper citation: DONE: Detection of Outlier NEurons (RRID:SCR_005299) Copy   

•   Source: SciCrunch Registry

http://www.youtube.com/user/WholeBrainCatalog?feature=autoshare

Videos uploaded to YouTube by the Whole Brain Catalog.

Proper citation: WholeBrainCatalog's Channel - YouTube (RRID:SCR_005436) Copy   

•   Source: SciCrunch Registry

https://www.mcdb.ucla.edu/Research/Hartenstein/dbla/index.html

Atlas providing structure and development of Drosophila brain lineages. Used to learn about projection pattern of lineages as first step towards reconstructing and understanding all neurons.

Proper citation: Drosphila Brain Lineage Atlas (RRID:SCR_017507) Copy   

•   Source: SciCrunch Registry

http://lfpy.github.io

Python package for calculation of extracellular potentials from multicompartment neuron models. LFPy can be used to set up a model, run simulations, and calculate the extracellular potentials arising from activity in the given model neuron. It relies on the Python interface provided by the NEURON simulator.

Proper citation: LFPy (RRID:SCR_014805) Copy   

•   Source: SciCrunch Registry

http://neurofitter.sourceforge.net

Neurofitter is software for parameter tuning of electrophysiological neuron models. It automatically searches for sets of parameters of neuron models that best fit available experimental data, and therefore acts as an interface between neuron simulators, like Neuron or Genesis, and optimization algorithms, like Particle Swarm Optimization, Evolutionary Strategies, etc.

Proper citation: Neurofitter (RRID:SCR_005843) Copy   

•   Source: SciCrunch Registry

http://learn.genetics.utah.edu/content/addiction/

A physiologic and molecular look at drug addiction involving many factors including: basic neurobiology, a scientific examination of drug action in the brain, the role of genetics in addiction, and ethical considerations. Designed to be used by students, teachers and members of the public, the materials meet selected US education standards for science and health. Drug addiction is a chronic disease characterized by changes in the brain which result in a compulsive desire to use a drug. A combination of many factors including genetics, environment and behavior influence a person's addiction risk, making it an incredibly complicated disease. The new science of addiction considers all of these factors - from biology to family - to unravel the complexities of the addicted brain. * Natural Reward Pathways Exist in the Brain: The reward pathway is responsible for driving our feelings of motivation, reward and behavior. * Drugs Alter the Brain's Reward Pathway: Drugs work over time to change the reward pathway and affect the entire brain, resulting in addiction. * Genetics Is An Important Factor In Addiction: Genetic susceptibility to addiction is the result of the interaction of many genes. * Timing and Circumstances Influence Addiction: If you use drugs when you are an adolescent, you are more likely to develop lifetime addiction. An individual's social environment also influences addiction risk. * Challenges and Issues in Addiction: Addiction impacts society with many ethical, legal and social issues.

Proper citation: New Science of Addiction: Genetics and the Brain (RRID:SCR_002770) Copy   

•   Source: SciCrunch Registry

http://www.cnl.salk.edu/

The long range goal of this laboratory is to understand the computational resources of brains from the biophysical to the systems levels. The central issues being addressed are how dendrites integrate synaptic signals in neurons, how networks of neurons generate dynamical patterns of activity, how sensory information is represented in the cerebral cortex, how memory representations are formed and consolidated during sleep, and how visuo-motor transformations are adaptively organized. Additionally, new techniques have been developed for modeling cell signaling using Monte Carlo methods (MCell) and the blind separation of brain imaging data into functionally independent components (ICA).

Proper citation: Computational Neurobiology Laboratory at the Salk Institute (RRID:SCR_002809) 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