Difference between revisions of "Analysis Package"
From UCLA Miniscope
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**Behavior tracking and syncing to microscope data | **Behavior tracking and syncing to microscope data | ||
**Activity detection | **Activity detection | ||
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| + | The Miniscope Analysis Github repository can be found [https://github.com/daharoni/Miniscope_Analysis.git here]. | ||
Revision as of 16:06, 8 January 2016
Overview
The MATLAB analysis package provided on this wiki contains the tools necessary for going from the raw microscope video to deconvolved individual neuron activity. Our package supports batch (generally run in parallel) processing for handling multiple recording sessions and can also be run on an individual session stored in the MATLAB workspace.
Roughly speaking the steps involved are the following
- Generate MATLAB data structure
- All access to the raw or processed data is handled through this data structure
- Memory efficient (less than 10MB per minute of recording)
- Correct for motion artifacts using an amplitude or FFT image registration algorithm
- Identify the bountries of individual neurons (we provide a novel, fully automated segmentation approach)
- Extract dF/F activity for identified neurons
- Deconvolve dF/F activity using Wiener or Fast Nonnegative deconvolution to approximate neuronal spiking pattern
- Additional Analysis:
- Cross-talk removal
- Segment matching across sessions
- Behavior tracking and syncing to microscope data
- Activity detection
The Miniscope Analysis Github repository can be found here.
Image Registration
We suggest using Dario Ringach's recursive image registration algorithm.
Automated Segmentation
