Many concepts in neuroscience can be condensed into a (series of) mathematical equation(s). A prominent example is the ordinary least-squares multiple regression case, which uses the outcome of the matrix inversion of the covariance matrix of the design:
The data (y) is being explained by the product of the regression weights (beta/s) and the design matrix (X), plus the residual error term (epsilon):
After solving the normal equation that suffices the condition that the sum of squares of errors (SSE) is minimized, the result is:
Of course, this is just an example! :)
 button from the toolbar above the article source code area to upload the image, choosing a unique name that is unlikely to clash with other image names, and then select the filename of the uploaded image to add the markup language into article sourceW.r.t. the formulas on this page, here is the code that was used to generate the images above:
y = X beta ~ + ~ varepsilon
hat{beta} = (X^T X)^ {-1} X^T y