  In daily life situations, we have to perform multiple tasks given a visual
stimulus, which requires task-relevant information to be transmitted through
our visual system. When it is not possible to transmit all the possibly
relevant information to higher layers, due to a bottleneck, task-based
modulation of early visual processing might be necessary. In this work, we
report how the effectiveness of modulating the early processing stage of an
artificial neural network depends on the information bottleneck faced by the
network. The bottleneck is quantified by the number of tasks the network has to
perform and the neural capacity of the later stage of the network. The
effectiveness is gauged by the performance on multiple object detection tasks,
where the network is trained with a recent multi-task optimization scheme. By
associating neural modulations with task-based switching of the state of the
network and characterizing when such switching is helpful in early processing,
our results provide a functional perspective towards understanding why
task-based modulation of early neural processes might be observed in the
primate visual cortex
