  Although deep neural networks (DNNs) have demonstrated impressive results
during the last decade, they remain highly specialized tools, which are trained
-- often from scratch -- to solve each particular task. The human brain, in
contrast, significantly re-uses existing capacities when learning to solve new
tasks. In the current study we explore a block-modular architecture for DNNs,
which allows parts of the existing network to be re-used to solve a new task
without a decrease in performance when solving the original task. We show that
networks with such architectures can outperform networks trained from scratch,
or perform comparably, while having to learn nearly 10 times fewer weights than
the networks trained from scratch.
