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Operation of Code


1. unpack the .zip into whatever directory you like.
2. open MATLAB and move into that directory.

Now, you're good to go.  To run the demos, run one of the lca_example scripts (i.e., type lca_example in the command window) from within MATLAB.

There are three example scripts provided.  These can be run without knowing anything about the program:.

lca_example.m -  this is an example of LCA with whitening.  The example develops 256 lobe components using randomly selected 16 by 16 pixel windows from each of 13 grayscale images of nature (located in data\naturalimages).   It runs over 1500000 samples, so it takes a little while.

lca_example2.m - same as above, but without whitening, and fewer samples (500000).
lca_example3.m - an example of topographic LCA with whitening.  Aside from the simulation length (500000), it's same as in the first example, but the adjacent lobe components in the grid will update along with the winning unit.
Usage
The function you want to use is lca.m - "help lca" will give detailed information about all inputs and outputs.  You can use the program with your own images or sythetic data in an image-like format - a 3D matrix where the first two dimensions give pixel row and column, and the third gives the number of samples.


For an overview of how the the algorithm works read below.  For a more detailed understanding, read the comments in the .m files.


Description of Code

Stages of the algorithm
1. Sampling - Because of the efficiency in which Matlab does matrix operations vs. vector operations within loops, this version of LCA samples data first, and preprocesses all at once.  The window size input to the program (the winsize parameter) gives the size of the square sampling region, which is selected randomly from all input images for a certain number of samples (given by the sampletime parameter).  Each sample is stored as a column vector in a sampling matrix.  The order of the sample columns are randomized before the next stage.
2. Preprocessing - In all cases, the local mean is subtracted from each sample.  If whitening is to be done (set by the pre parameter), the whitening and dewhitening matrices are computed here.  This can be done incrementally, using the CCIPCA algorithm, but is not here, again due to the efficiency of matrix operations vs. loops in Matlab.  Finally, the samples are whitened through multiplication of the sample matrix and the whitening matrix.
3. Initialization - The lobe components (the number of which is set by the numn parameter) are initialized to the first samples.
4. Loop: competition and updating - for each sample, the response is computed for all lobe components, then sorted.  The numwinners lobe components with the largest absolute responses will update using amnesic averaging.  If the topography parameter was set, the grid neighbors of each updating lobe component will also update.
5. Resampling and preprocessing - the same number of samples is collected randomly from the inputs.  The local mean is subtracted from each, and they are all whitened using the old whitening matrix, if necessary.  The whitening matrix is not recomputed, so make sure to set sampletime to a large enough value if you're using whitening.  The competition loop continues for another iteration (the total number of iterations is set by the iteration parameter).
6. Loop Completion - the lengths of the lobe components reflect the energy, or variance, of the samples they updated for.  For filter weights that relate to the input (i.e. pixels in the range of 0 - 255), the standard deviation is needed.  So each vector is divided by the square root of its length.

The lobe component weights and number of times each updated are returned as output values.  If the display parameter is set, the program will lastly display the lobe components as images, and display the histogram of the number of times each updated.  The first figure is sorted by number of updates if topography is not used.  The second figure is always sorted to display the largest values on the left.





References  

J. Weng, N. Zhang and R. Gajakunta, "Distribution Approximation: An In-Place Developmental Algorithm for Sensory Cortices and A Hypothesis" Technical Report MSU-CSE-4-40. Computer Science and Engineering, Michigan State University, East Lansing, Michigan, September 2004.  Download PDF file.

J. Weng and M. D. Luciw, ``Optimal In-Place Self-Organization for Cortical Development: Limited Cells, Sparse Coding and Cortical Topography,''  in Proc. 5th International Conference on Development and Learning, May 30 - June 3, Bloomgton, IN, 2006. Download PDF file.


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Email inquiries about this page to: luciwmat@cse.msu.edu
Embodied Intelligence Laboratory
Department of Computer Science and Engineering
Michigan State University