Neuroscience is surprisingly connected to mathematics. In many neuroscience studies, you record thousands of data points all at once. To find patterns within this seemingly sporadic data, neuroscientists utilize a PCA (Principle Component Analysis).
The way PCA works is by computing eigenvectors of a covariance matrix. Eigenvectors are special types of vectors that only stretch or shrink, without changing directions. The degree to which these vectors are stretched or shrunk is called the eigenvalue, denoted by the greek letter lambda. Eigenvectors reveal the natural coordinate system of another system. It depicts the overall direction in which a system wants to veer towards. The details of how PCA utilizes eigenvectors to analyze neuroscience data is a bit beyond my understanding. But, there always exists an interesting connection between the life sciences, mathematics, and computational statistics.
Daily Erudition: Learning Something Every Day 