  A key property of neural networks (both biological and artificial) is how
they learn to represent and manipulate input information in order to solve a
task. Different types of representations may be suited to different types of
tasks, making identifying and understanding learned representations a critical
part of understanding and designing useful networks. In this paper, we
introduce a new pseudo-kernel based tool for analyzing and predicting learned
representations, based only on the initial conditions of the network and the
training curriculum. We validate the method on a simple test case, before
demonstrating its use on a question about the effects of representational
learning on sequential single versus concurrent multitask performance. We show
that our method can be used to predict the effects of the scale of weight
initialization and training curriculum on representational learning and
downstream concurrent multitasking performance.

Machine Learning;Neural and Evolutionary Computing