This code implements the simulation results reported in:

M. W. Spratling (2012) Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function, Neural Computation, 24(1): 60-103.

Please cite this paper if this code is used in, or to motivate, any publications.

---

USAGE

---

This code was tested with MATLAB Version 7.10 (R2010a).

To use this software:

    run matlab 
    >> cd to the directory containing this code 

To perform the experiments on learning the bars problem using a single PC/BC processing stage (as described in section 3.1 of the paper), do the following:

   >> [X,W,V,U]=learn_bars_feedback;

To change the version of the bars problem, the size of the network, the amount of noise, or other parameter values, you should edit this script. The script META_learn is useful for performing multiple trials automatically.

To perform the experiments on learning natural images using a single PC/BC processing stage (as described in section 3.2 of the paper), do the following.

To learn the RFs:

>> [X,W,V,U]=learn_natural_image_patches; 

To change the size of the image patches, to change other parameter values, or the name of the image data file you should edit this script. Somewhere on your MATLAB path you will need a set of greyscale natural images stored in a .mat file.

To reconstruct the RFs using reverse correlation:

>> [RF]=reverse_correl(W,V,1.5);

Note, this requires the image patches, and hence the weights learnt from them, to have an odd size (e.g. 11x11 pixels, not 12x12 pixels).

To fit Gabor functions to the reconstructed RF:

>> [best_fit,best_fit_params]=fit_gabor_with_ga(RF,[11,11],1.5);

This makes use of the GAOT toolbox, which must therefore also be on your path. The GA often produces poor fits, to improve the results you might use the functions META_fit_gabor_with_ga and/or combine_ga_results to get the best fits taken from a number of runs of the GA.

To measure the sparsity and selectivity of the trained network:

>> measure_sparsity([],W,V);

To plot conditional response histograms:

>> measure_conditional_response(W,V,node);

where node is the index of the prediction neuron you want to plot the histograms for. To perform image reconstruction:

>> measure_reconstruction_quality(W,V,I);

where I is the greyscale image you wish to reconstruct. To measure the NMSE between random image patches and their reconstructions:

>> measure_reconstruction_error(W,V);

To perform the experiments on learning natural images using a hierarchical PC/BC network (as described in section 3.3 of the paper), do the following.

To learn the RFs:

>> [X,Wrecon,Vrecon,Urecon,W,V,U]=learn_natural_image_patches_hierarchy;

To test the response properties using the Ito and Komatsu angles dataset:

>> test_response_of_V2(W,V,U);

This function makes use of nansuite toolbox by Jan Glaescher, available here: http://www.mathworks.com/matlabcentral/fileexchange/6837

To assess the response properties of neurons with V1-like RFs (as described in both section 3.2 and 3.3 of the paper), do the following:

>> V1_orientation_tuning_contrast(W,V,node,best_params);
>> V1_size_tuning(W,V,node,best_params);
>> V1_spatial_frequency_tuning_contrast(W,V,node,best_params);
>> V1_temporal_frequency_tuning(W,V,node,best_params);
>> V1_cross_orient_orient(W,V,node,best_params);

Where W and V are the weights for the model of V1 learnt using either learn_natural_image_patches (W and V) or learn_natural_image_patches_hierarchy (W{1} and V{1}), node is the index of the prediction neuron whose response is to be recorded, and best_params is vector defining the parameters of the sinusoidal grating that produces the maximal response from the recorded node, which can be found using:

>> best_params=V1_best_params(W,V,node);