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Accuracy Maximization Analysis implementation for AMA-SGD and AMA-Gauss
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============ = Overview = ============ This repository provides a MATLAB implementation for learning the optimal receptive fields for specific tasks using Accuracy Maximization Analysis (AMA). Code is provided for +) AMA-SGD: an implementation employing stochastic gradient descent (SGD) as described in the paper: Johannes Burge & Priyank Jaini (submitted) “Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Priors, and Coding Advantages for Scaled Additive Noise” +) AMA-Gauss: an implementation employing the assumption that the class-conditional distributions are Gaussian distributed as described in the paper: Priyank Jaini & Johannes Burge (submitted) “Accuracy Maximization Analysis with Class-Conditional Gaussian distributions: Linking Normative Models to Energy Models and Methods for Neural Systems Identification” +) AMA: the original method as described in the paper: Wilson S. Geisler, Jiri Najemnik, Almon D. Ing (2009) “Optimal stimulus encoders for natural tasks” Journal of Vision, 9(13):17, 1-16 and first code release as described in the paper: Johannes Burge & Wilson S. Geisler (2011) “Optimal defocus estimation in individual natural images” Proceedings of the National Academy of Sciences, 108(40): 16849-16854 Please cite the appropriate work if you use this data in your research. The repository also contains training sets of natural stimuli for two visual tasks: (+) binocular disparity estimation as described in the paper: Johannes Burge & Wilson S. Geisler (2014) “Optimal disparity estimation in natural stereo-images” Journal of Vision, 14(2):1, 1-18 (+) retinal speed estimation as described in the paper: Johannes Burge & Wilson S. Geisler (2015) “Optimal speed estimation in natural image movies predicts human performance” Nature Communications, 6:7900, doi:10.1038/ncomms8900 Please cite the appropriate work if you use this data in your research. ======================= = System Requirements = ======================= The code has been tested on MATLAB 2011a and later versions. Troubleshooting (Matlab R2011a, R2011b, R2012a and Xcode 4.2, Xcode 4.3). Any combination of these versions of Mathworks & Apple software may prevent Matlab from finding the C++ header files. To fix the issue, go to http://www.mathworks.com/support/solutions/en/data/1-FR6LXJ/ and follow the instructions ============ = Datasets = ============ 1) Binocular Disparity Estimation: AMAdataDisparity.mat 2) Retinal Speed Estimation: AMAdataSpeed.mat ================== = Using the code = ================== Step 1: Save the code repository and add it to your matlab path Step 2: Compile the C files. Type the following commands at the prompt: mex AMAengine.cpp mex AMAengineGradientMAP.cpp mex AMAengineGradientMSE.cpp Step 3: Load the desired dataset using the file loadAMAdata.m % LOAD DISPARITY DATA [s, ctgInd, X] = loadAMAdata(‘Disparity’) % LOAD SPEED DATA [s, ctgInd, X] = loadAMAdata(‘Speed’) Step 4: Run amaR01.m. % EXAMPLE CALLS: % LEARN 2 FILTERS SEQUENTIALLY W. FULL MODEL [f E minTimeSec] = amaR01('FLL','MAP',2,0,1,,s,ctgInd,X,5.7,0.5,0.23,5); % LEARN 2 FILTERS SIMULATANEOUSLY W. FULL MODEL [f E minTimeSec] = amaR01('FLL','MAP',2,0,2,,s,ctgInd,X,5.7,0.5,0.23,5); % LEARN 2 FILTERS SIMULATANEOUSLY W. STOCHASTIC GRADIENT DESCENT [f E minTimeSec] = amaR01('SGD','MAP',2,0,2,,s,ctgInd,X,5.7,0.5,0.23,5,570,15,.1,.001,.01); % LEARN 2 FILTERS SIMULTANEOUSLY W. GAUSSIAN MODEL [f E minTimeSec] = amaR01('GSS','MAP',2,0,2,,s,ctgInd,X,5.7,0.5,0.23,5); Note: To avoid non-global local minima in the optimization process, best practice is to initialize the filter learning process several times with different random seeds Step 5: Plot the filters using the function plotFilters.m % EXAMPLE CALLS: % PLOT DISPARITY FILTERS plotFilters('Disparity',f,); % PLOT SPEED FILTERS plotFilters('Speed',f,); ========================= = Function Descriptions = ========================= All Matlab functions in this repository contain detailed descriptions of the input and output parameters