UCLA

To act and survive in an environment, humans and other organisms need to form useful representations of it. Such representations are formed through co-ordinated transformations across areas (or layers), and often change through developmental experience. We find internal representations in trained deep neural networks capture the key features of multi-area neural recordings during a perceptual decision-making task, where minimal sufficient representations of sensory information emerge along a cortical hierarchy. Using our models, we show that these minimal sufficient representations emerge through preferential propagation of task-relevant information between areas.\n To better understand how such representations emerge through learning, we introduce a notion of usable information, and use it to show that a noisy learning process (e.g. Stochastic Gradient Descent) plays an important role in forming these minimal sufficient representations. We find that the learning process is highly nonlinear: semantically meaningful information is initially encoded in the representation, even if it is not needed for the task. Additionally, we show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early stages of learning. In particular we find, using analytical models and through simulations, that depth and competition between sources has a significant effect on critical learning periods observed in biological and artificial networks. \n We further study how multisensory information can be decomposed, and develop novel approximations to compute the redundant information shared between a set of sources about a target, and show that the common information shared between a set of sources can be used to guide the learning of meaningful representations.