This code implements the simulation results reported in:

M. W. Spratling (2012) Predictive coding as a model of the V1 saliency map hypothesis, Neural Networks, 26:7-28.

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

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USAGE

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This code requires MATLAB and the Image Processing Toolbox. It was tested with MATLAB Version 7.7 (R2008b) and Image Processing Toolbox Version 6.2 (R2008b).

To use this software:

    run matlab 
    cd to the directory containing this code 
    run one of the functions with the name V1_*

Each of the V1_* functions runs one of the experiments reported in the above publication. The following table gives details of which MATLAB function produces each figure in this article.

Simulations will run much faster if you install the MATLAB function convnfft written by Bruno Luong, which is available here: http://www.mathworks.com/matlabcentral/fileexchange/24504-fft-based-convolution

Figure	MATLAB Function
3a	V1_bar_and_texture_errors
3b	V1_texture_border_errors
4a	V1_border_orientation
4b	V1_border_orientation (uncomment the line beginning: angle_jitter=15)
5a	V1_border_spacing
5b	V1_border_length
6	V1_border_spacing_luminance
7	V1_border_zhaoping_may
8	V1_feature_efficiency
9a	V1_feature_category
9b	V1_feature_symmetry
10	V1_feature_efficiency_circles
11	V1_feature_efficiency_luminance
12a	V1_feature_asymmetric1
12b	V1_feature_asymmetric2
13	V1_asymmetric_feature_errors
14	V1_feature_spacing
15	V1_feature_zhaoping_may
16	V1_feature_onset
17	V1_feature_preview
18	V1_change_blindness
19b	V1_border_attention
20	V1_contour
21	V1_feature_attention