GPmaps: Estimating Orientation Preference Maps using Gaussian Process Methods


MATLAB code implementing the Gaussian Process based methods for estimating orientation preference maps from optical imaging data as described in Macke et al, 2009 and Macke at al, 2010. The package contains code for
  1. Generating synthetic orientation preference maps and noisy imaging measurements
  2. Estimating orientation preference maps using Gaussian process methods or vector averaging
  3. Sampling from the posterior distribution over maps
While the current implementation is useful for orientation preference maps only, it should be straightforward to adapt it for the estimation of other cortical maps as well.


To install the code, just unzip the file and add all the folders originating from it to your MATLAB path. Then, run the script DemoScript.m, which contains a brief tutorial on how to use the methods.


This file contains all the functions necessary to run the Gaussian Process methods, as well as the demo-file DemoScript.m, which contains a brief tutorial on how to use the methods.

The code is published under the GNU Gneral Public License. The code is provided "as is" and has no warranty whatsoever.
Imaging data (optical imaging of intrinsic signals) used in the paper. The data is already normalized, and consists of one four-dimensional array of dimensions 126 by 252 by 8 by 100. The first two dimensions are pixels, the third dimension stimulus conditions, and the fourth dimension trials.


J. H. Macke, S. Gerwinn, M. Kaschube, L. E. White, and M. Bethge
Bayesian estimation of orientation preference maps
Advances in Neural Information Processing Systems 22, 2009
#bayesian inference, #orientation maps
Code, PDF, BibTex

J. H. Macke, S. Gerwinn, L. White, M. Kaschube, and M. Bethge
Gaussian process methods for estimating cortical maps
NeuroImage, 56(2), 570-581, 2010
#gaussian process
Code, URL, DOI, PDF, BibTex
University of Tuebingen BCCN CIN MPI