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# All the model and the analyses were implemented in MATLAB. -getActiveUnits.m ---This function return the active units indexes according to the output of a layer. -getLifeSparse.m ---This function return the lifetime sparseness of each unit according to the output of a layer. -getPSI.m ---This function return the PSI matrix according to the distributions of every unit's responses to every phoneme. -getSTRF.m ---This function return the strfs of each unit in the first 3 S layers according to the model parameters -getSTRFFeatures ---This function return the 4 STRF features according to a STRF -main.m ---This is the script file for SHMAX setup, training and inference. The corresponding parameters are determined in the "setParameters.m". -sample3dImages.m ---This function return random 3D patches from the data -sampleImages.m ---This function return random 2D patches from the data -SHMAX_2dS.m ---This function return the bases and do the inference according to the model parameters, which is used for 2D inputs. ---The training algorithm is implemented by the "SPAMS"*. -SHMAX_3dS.m ---This function return the bases and do the inference according to the model parameters, which is used for 3D inputs. -SHMAX_C.m ---This function do the max pooling according to the model parameters. -setParameters.m ---This is a script file that determines the parameters used for build the deep SHMAX model. * SPAMS: http://spams-devel.gforge.inria.fr/
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Using an deep SHMAX model to predict auditory pathway neuron behaviors
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