Tutorial describing the basic search and navigation features of the Allen Mouse Brain Atlas
Tutorial describing the basic search and navigation features of the Allen Developing Mouse Brain Atlas
This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain and to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.
The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.
GeneWeaver is a web application for the integrated cross-species analysis of functional genomics data to find convergent evidence from heterogeneous sources. The application consists of a large database of gene sets curated from multiple public data resources and curated submissions, along with a suite of analysis tools designed to allow flexible, customized workflows through web-based interactive analysis or scripted API driven analysis. Gene sets come from multiple widely studied species and include ontology annotations, brain gene expression atlases, systems genetic study results, gene regulatory information, pathway databases, drug interaction databases and many other sources. Users can retrieve, store, analyze and share gene sets through a graded access system. Analysis tools are based on combinatorics and statistical methods for comparing, contrasting and classifying gene sets based on their members.
EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS X.
This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography
An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.
Demo of the BRIAN Simulator. BRIAN is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.
NeuroFedora is a volunteer driven initiative to provide a ready to use Fedora based Free/Open Source Software platform for neuroscience. We believe that similar to Free Software, science should be free for all to use, share, modify, and study. The use of Free Software also aids reproducibility, data sharing, and collaboration in the research community. By making the tools used in the scientific process easier to use, NeuroFedora aims to take a step to enable this ideal. The CompNeuro Fedora Lab was specially to enable computational neuroscience. It includes everything you will need to get your work done—modelling software, analysis tools, general productivity tools—all well integrated with the modern GNOME platform to give you a complete operating system.