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Due to uncertainty in inputs, we performed sensitivity analyses.
In order to assess the impact of statistical uncertainty around key model inputs, we performed a series of univariate and probabilistic sensitivity analyses.
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As the above-chance level decoding performance in the occipital cortex could arise from a gradual increase in overall BOLD responses as a result of the increase in visual input, we performed another GLM analysis for the main scans.
Using the genome map assembly as input, we performed structural variation detection, and the genome maps were compared to hg19.
For the 100 cell equivalent input we performed RNA-Amp™ on 1 ng of purified RNA from each cell line (5 replicates for each cell line), which was then analysed on Affymetrix arrays as described above.
To assess the effect of input uncertainty, we performed multiple sensitivity analyses.
In order to provide the necessary large input sets, we performed the ECP analyses on the three kingdoms of life instead of individual species.
In order to investigate the effects of the input addresses on the performance, we performed the lookups using pseudo-random generated datasets containing 226 IP addresses with different matching ratios and the AS65000 prefix database.
To examine the dependence of visually driven activity in areas V2 and V3 on input from V1, we performed fMRI experiments in two anesthetized monkeys (L02 & Q02) with area V1 lesions induced by aspiration (Materials and Methods).
DOI: http://dx.doi.org/10.7554/eLife.07290.005 To correlate specific movements with granule cell input and output, we performed activity-triggered averaging of motion maps in video images of locomoting mice.
As the result of these simulations depends on the average correlations between neighboring voxels derived from the statistical map given as input to AlphaSim, we performed these simulations separately for each statistical test.
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