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https://neuron.yale.edu/neuron/

Software for computational neurophysiology. Simulation environment is used for building and using computational models of neurons and networks of neurons. NEURON Users Group can participate in collaborative development of documentation, tutorials, and software.

Proper citation: NEURON (RRID:SCR_017449) Copy   

•   Source: SciCrunch Registry

https://github.com/padster/pyDynamo/

Software tool for neuron timelapse reconstruction, registration and analysis for Dynamic Morphometrics.

Proper citation: Dynamo (RRID:SCR_017541) Copy   

•   Source: SciCrunch Registry

http://neuronbank.org/wiki

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. Platform for Neuroscientists to describe neurons and neural circuitry. Registered users may edit. The ultimate goal is advance the field of Neuromics by creating an encyclopedia of neurons and neural circuitry. NOTE: The database is no longer being maintained due to lack of funding.

Proper citation: NeuronBank (RRID:SCR_002004) Copy   

•   Source: SciCrunch Registry

http://research.mssm.edu/cnic/

Center to advance research and training in mathematical, computational and modern imaging approaches to understanding the brain and its functions. Software tools and associated reconstruction data produced in the center are available. Researchers study the relationships between neural function and structure at levels ranging from the molecular and cellular, through network organization of the brain. This involves the development of new computational and analytic tools for imaging and visualization of 3-D neural morphology, from the gross topologic characteristics of the dendritic arbor to the fine structure of spines and their synapses. Numerical simulations of neural mechanisms based on these structural data are compared with in-vivo and in-vitro electrophysiological recordings. The group also develops new theoretical and analytic approaches to exploring the function of neural models of working memory. The goal of this analytic work is to combine biophysically realistic models and simulations with reduced mathematical models that capture essential dynamical behaviors while reproducing the functionally important features of experimental data. Research areas include: Imaging Studies, Volume Integration, Visualization Techniques, Medial Axis Extraction, Spine Detection and Classification, Applications of Rayburst, Analysis of Spatially Complex Structures, Computational Modeling, Mathematical and Analytic Studies

Proper citation: Computational Neurobiology and Imaging Center (RRID:SCR_013317) Copy   

•   Source: SciCrunch Registry

http://www.compneuro.org/CDROM/catacomb/index.html

Catacomb consists of a set of frameworks for various types of models in neuroscience, user interfaces to facilitate building models within these frameworks, and numerical algorithms to compute their behavior. The available frameworks include reaction kinetics, reaction diffusion systems, kinetic scheme models of ion channels, small neuron models and integrate and fire networks. It is a library of models (data structures and algorithms) covering a range of problems in neuroscience together with a versatile graphical user interface for constructing and running specific instances of the models. Some features of Catacomb include: * Class models. There is a growing set of classes containing data structures and calculation methods for various problem domains in neuroscience - reaction schemes, stochastic channel models, integrate-and-fire networks, cell geometry et al. * Dynamic interface builder. Using Java''s reflection capabilities, individual user interfaces are constructed for each class model allowing new instances to be created and displaying the results of any calculation methods they may contain. * Parameter watching. Using Java threads, the calculations are rerun and results displayed whenever parameters upon which they depend are changed. * Session recording. Operations can be recorded and played back for illustrating how to use Catacomb or for preconfigured demonstrations of model behavior. * Compatible with JPython. Catacomb does not include its own interpreter except in the minimal sense required to parse its own saved files. But its objects and methods are accessible to JPython which can be used for command line access or scripting. * Applet building. The contents of windows in the display can be extracted and packaged together in a single panel for loading as an applet. The model is saved as a java source file which, once compiled, can be packaged with the original Catacomb archive for loading from a Web page. See the AppletConfigEditor.

Proper citation: Components And Tools for Accessible COmputer Modeling in (neuro)Biology (RRID:SCR_008321) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/exonarray/cgi-bin/plot_selector.pl

Transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes. Provides improved cell-type-specific markers for better understanding of neural development, function, and disease.

Proper citation: Exon Array Browser (RRID:SCR_008712) Copy   

•   Source: SciCrunch Registry

http://www.functionalneurogenesis.com/blog/

A blog focusing on the function of adult neurogenesis in the dentate gyrus of the hippocampus, including discussion of scientific research papers, methods and protocols, and other trends or observations about the field.

Proper citation: Functional Neurogenesis (RRID:SCR_008830) Copy   

•   Source: SciCrunch Registry

https://github.com/scidash/neuronunit

Software toolkit for data-driven validation of neuron and ion channel models using SciUnit. NeuronUnit implements an interface to several simulators and model description languages, handles test calculations according to domain standards, and enables automated construction of tests based on data from several major public data repositories.

Proper citation: NeuronUnit (RRID:SCR_015634) Copy   

•   Source: SciCrunch Registry

http://data.neuinfo.org/modelrun

Data set of output of neuron models through the Trestles supercomputer.

Proper citation: ModelRun (RRID:SCR_001532) Copy   

•   Source: SciCrunch Registry

http://neomorph.salk.edu/brain_methylomes/

Datasets described in the manuscript: "Global Epigenomic Reconfiguration During Mammalian Brain Development" (Science, 2013 - DOI: 10.1126/science.1237905. This study provides genome-wide composition, patterning, cell specificity, and dynamics of DNA methylation at single-base resolution in human and mouse frontal cortex throughout their lifespan. Widespread methylome reconfiguration occurs during fetal to young adult development, coincident with synaptogenesis.

Proper citation: Mammalian Brain Methylomes (RRID:SCR_001648) Copy   

•   Source: SciCrunch Registry

http://blog.wholebraincatalog.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. The blog of the Whole Brain Catalog.

Proper citation: Whole Brain Catalog Blog (RRID:SCR_000582) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/kravitz2

Dataset of the spike and laser timestamps from Kravitz, Owen and Kretizer's 2012 paper "Optogenetic identification of striatal projection neuron subtypes during in vivo recordings." The code will analyze spike trains around laser pulses to determine if a cell is significantly activated by the laser, and therefore expresses an excitatory opsin, such as channelrhodopsin-2. It returns an excel sheet that simply identifies the activated cells.

Proper citation: Kravitz Dataset 2 (RRID:SCR_000296) Copy   

•   Source: SciCrunch Registry

http://www.linked-neuron-data.org/

Neuroscience data and knowledge from multiple scales and multiple data sources that has been extracted, linked, and organized to support comprehensive understanding of the brain. The core is the CAS Brain Knowledge base, a very large scale brain knowledge base based on automatic knowledge extraction and integration from various data and knowledge sources. The LND platform provides services for neuron data and knowledge extraction, representation, integration, visualization, semantic search and reasoning over the linked neuron data. Currently, LND extracts and integrates semantic data and knowledge from the following resources: PubMed, INCF-CUMBO, Allen Reference Atlas, NIF, NeuroLex, MeSH, DBPedia/Wikipedia, etc.

Proper citation: Linked Neuron Data (RRID:SCR_003658) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/StemCellInfo

Data tables providing an overview of information about stem cells that have been derived from mice and humans. The tables summarize published research that characterizes cells that are capable of developing into cells of multiple germ layers (i.e., multipotent or pluripotent) or that can generate the differentiated cell types of another tissue (i.e., plasticity) such as a bone marrow cell becoming a neuronal cell. The tables do not include information about cells considered progenitor or precursor cells or those that can proliferate without the demonstrated ability to generate cell types of other tissues. The tables list the tissue from which the cells were derived, the types of cells that developed, the conditions under which differentiation occurred, the methods by which the cells were characterized, and the primary references for the information.

Proper citation: National Institutes of Health Stem Cell Tables (RRID:SCR_008359) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/L-Neuron/database/

A database of virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons. It presently contains 542 cells. In the trade neurons collection the database contains an amaral cell archive, neuron morpho reconstructions, and mouse alpha motoneurons. Their collection of generated neurons include motoneurons, Purkinje cells, and hippocampal pyramidal cells.

Proper citation: Virtual NeuroMorphology Electronic Database (RRID:SCR_007118) Copy   

•   Source: SciCrunch Registry

https://senselab.med.yale.edu/MicroCircuitDB/

A database for storing and efficiently retrieving realistic computational models of brain microcircuits and networks. The focus is on microcircuits that are based on experimentally demonstrated properties of neurons and their connectivity.

Proper citation: MicrocircuitDB (RRID:SCR_014577) Copy   

•   Source: SciCrunch Registry

https://ipsc.bsd.uchicago.edu/

Core provides training to use latest episomal techniques to reprogram, expand and characterize human and mice iPS cells from skin or blood tissues of healthy subjects and diseased patients. Develops capability to differentiate iPS cells into specific somatic cells, such as neutrons, cardiomyocytes, and hepatocytes.

Proper citation: Chicago University iPSC Core Facility (RRID:SCR_017918) Copy   

•   Source: SciCrunch Registry

http://johnhommer.com/academic/code/cnrun/

Neuronal network model simulator for both individual neurons and networks of neurons. It uses NeuroML for network description. Various neuron and synapse model types are supported. CNN features include: an integration cycle which uses a Runge–Kutta 6-5-order method, extensive scripting capabilities, individual unit state introspection from scripts, and neuroplastic processes identified by label or regexp.

Proper citation: CNrun (RRID:SCR_014761) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/brainsinsilicon/neurogrid.html

A specialized hardware platform that will perform cortex-scale emulations while offering software-like flexibility. With sixteen 12x14 sq-mm chips (Neurocores) assembled on a 6.5x7.5 sq-in circuit board that can model a slab of cortex with up to 16x256x256 neurons - over a million! The chips are interconnected in a binary tree by 80M spike/sec links. An on-chip RAM (in each Neurocore) and an off-chip RAM (on a daughterboard, not shown) softwire vertical and horizontcal cortical connections, respectively. It provides an affordable option for brain simulations that uses analog computation to emulate ion-channel activity and uses digital communication to softwire synaptic connections. These technologies impose different constraints, because they operate in parallel and in serial, respectively. Analog computation constrains the number of distinct ion-channel populations that can be simulatedunlike digital computation, which simply takes longer to run bigger simulations. Digital communication constrains the number of synaptic connections that can be activated per secondunlike analog communication, which simply sums additional inputs onto the same wire. Working within these constraints, Neurogrid achieves its goal of simulating multiple cortical areas in real-time by making judicious choices.

Proper citation: Neurogrid (RRID:SCR_005024) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/NIF-Ontology

The NIF Standard Ontology (NIFSTD) is a collection of modular ontologies that provides an extensive set of terms and concepts important for the domains of neuroscience and biology, as well as the data and resources relevant for the life sciences. It is a core component of the Neuroscience Information Framework (NIF) project, a semantically enhanced portal for accessing and integrating neuroscience data, tools and information.

Proper citation: NIFSTD (RRID:SCR_005414) Copy   

•   Source: SciCrunch Registry