In this course, you will learn about working with calcium-imaging data, including image processing to remove background "blur", identifying cells based on threshold spatial contiguity, time-series filtering, and principal component analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.
Module 5: Calcium Imaging
Course Features
Videos
Tutorials
Exercises
Code / Datasets
Lessons of this Course
1
1
Duration:
5:02
Speaker:
This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.
2
2
Duration:
15:01
Speaker:
This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.
3
3
Duration:
5:15
Speaker:
This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.
4
4
Duration:
17:08
Speaker:
In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.
5
5
Duration:
11:23
Speaker:
This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.
6
6
Duration:
22:41
Speaker:
This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.
7
7
Duration:
17:19
Speaker:
This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.
