We report an open source toolbox for automating the scoring of several common behavior tasks used by the neuroscience community. Software package (MATLAB source code files and standalone executables) can be downloaded using the links below. Latest version is 15.03 (released in March 2015). Please view the accompanying manuscript in Frontiers in Behavioral Neuroscience and the Getting Started user guide below for instructions on use and installation.
Please visit the links below for details on how to run the software to analyze specific behaviors. For each behavior, we include detailed step-by-step instructions, video demonstration of use of the software, and sample experiment video for you to try running the analysis on your computer.
We have developed a set of MATLAB routines and a graphical user interface to facilitate the analysis of fluorescent calcium imaging of neuronal networks. The Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP) allows users to:
The MATLAB source code can be downloaded below. Please read the User Guide and our accompanying publication in the Journal of Neuroscience Methods for more details. To get started, simply download the source code, extract the zip file, run MATLAB and change directory to FluoroSNNAP. Enter FluoroSNNAP at the command window to start the software.
A sample experiment of the spontaneous activity of cultured primary rat cortical neurons is also made available to test the software. For this experiment, neurons were transduced with AAV2-GCaMP6 and the activity was measured at 10Hz for 2 minutes. Note: the sample experiment, titled "baseline.tif", is a large file (800MB) so be patient when downloading. Alternatively, use the baseline.csv file which contains fluorescence vs. frame data for each neuron in the field view as a comma-separated format (columns = neurons, rows = frames).
Screencast showing the use of FluoroSNNAP on a Macbook Pro with MacOSX10.9, 2.5GHz core i7 processor and 16 GB RAM, running MATLAB 2014a:
If your computer does not have sufficient memory, the ICA based batch segmentation feature may fail and MATLAB may freeze. In this case, you may still continue to use the software by choosing semi-automated segmentation methods - thresholding, and active coutour evolution. The screencast below demonstrates these features. It was recorded on a Windows 7, i7 930 computer with 12GB RAM, running MATLAB 2013b.