How biological attention mechanisms improve task performance in a large-scale visual system model

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.

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Replicates
 You should report how often each experiment was performed  You should include a definition of biological versus technical replication  The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates  If you encountered any outliers, you should describe how these were handled  Criteria for exclusion/inclusion of data should be clearly stated  High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: The number of images and image categories used in each task is giving the corresponding Methods section. Outliers were displayed as dots in box and whisker plots (as seen in Figs 1, 2, and 5). In the orientation detection task, the vertical orientation was removed from the analysis because the pre-attention performance level was an outlier (explained in Sec. 2.4) Statistical reporting  Statistical analysis methods should be described and justified  Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10)  For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d)  Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
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Group allocation
 Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied  Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Additional data files ("source data")  We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table  Where provided, these should be in the most useful format, and they can be uploaded as "Source data" files linked to a main figure or table  Include model definition files including the full list of parameters used  Include code used for data analysis (e.g., R, MatLab)  Avoid stating that data files are "available upon request" eLife Sciences Publications, Ltd is a limited liability non-profit non-stock corporation incorporated in the State of Delaware, USA, with company number 5030732, and is registered in the UK with company number FC030576 and branch number BR015634 at the address 1st Floor, 24 Hills Road, Cambridge CB2 1JP | August 2014 All errorbars are labeled in corresponding figure legends (usually SEM). P-values are provided in Figure 3 and 5 legends for comparison between performance with 2 different attention methods.
The simulations in this study do not require this 3 Please indicate the figures or tables for which source data files have been provided: Model weights (for VGG-16) are already publicly available (and referenced). Data analysis code is in preparation to be publicly posted.
eLife Sciences Publications, Ltd is a limited liability non-profit non-stock corporation incorporated in the State of Delaware, USA, with company number 5030732, and is registered in the UK with company number FC030576 and branch number BR015634 at the address 1st Floor, 24 Hills Road, Cambridge CB2 1JP | August 2014