DOI: 10.5061/dryad.jc14081
title: Data from: How biological attention mechanisms improve task performance in a large-scale visual system model

description: How does attentional modulation of neural activity enhance performance?
 Here we use a deep convolutional neural network as a large-scale model of
 the visual system to address this question. We model the feature
 similarity gain model of attention, in which attentional modulation is
 applied according to neural stimulus tuning. Using a variety of visual
 tasks, we show that neural modulations of the kind and magnitude observed
 experimentally lead to performance changes of the kind and magnitude
 observed experimentally. We find that, at earlier layers, attention
 applied according to tuning does not successfully propagate through the
 network, and has a weaker impact on performance than attention applied
 according to values computed for optimally modulating higher areas. This
 raises the question of whether biological attention might be applied at
 least in part to optimize function rather than strictly according to
 tuning. We suggest a simple experiment to distinguish these alternatives.
description: Object Category Tuning
 Curves and GradientsThis file contains
 tuning curves made from responses (to regular images) to the 20 object
 categories used. Four types of gradient values are also included defined
 according to images used (1=merged, 2=array) and classifier (binary or
 1000-way)object_GradsTCs.zipBinary Object ClassifiersTensorflow weights for each of the 20 binary classifiers (1 for each category) that are used to replace the original 1000-way classifiercatbins.zipPerformance on Object Detection TasksThese files contain true positive and true negative rates for attention applied in different ways to each of 20 categoriesobjperf.zipCNN weightsThe tensorflow weights for the full VGG-16 convolutional neural network used in this study (originally provided by Davi Frossard)vgg16_weights.npzOrientation Binary ClassifiersTensorflow weights that replace final 1000-way classifier with a series of binary classifiers for different orientationsori_catbins.zipOrientation Tuning Curves and GradientsTuning curve values and binary detection gradient values for oriented grating stimuliori_TCGrads.zipPerformance on Orientation Detection TasksFiles containing true positive and true negative performance values for attention applied in different waysoriperf.zipOrientation Task ImagesThis file contains images with two orientated stimuli in them along with information on the stimuli locations, orientations, and colorStim2Constr540_oriims.npzImages for Object taskMerged and Array object category imagesobjims.zipRecorded Activity during Oriented StimuliEach of the files in this folder (named according to the orientation and layer attention was applied to) contains activity recorded from each feature map, averaged over spatial dimensions and images. It is arranged in a list that contains 5 numpy arrays (1 for each of the sets of convolutional layers between pooling layers). Each numpy array is of dimensions: 2 or 3 (depending on the number of convolutional layers in that part of the architecture) x number of feature maps at that layer x orientation presented (9) x strength of attention. The tuning values are also included as they are needed to make Figure 6ori_activity.zip