Athena Akrami, Learning, Inference & Memory Lab, Sainsbury Wellcome Centre, UCL
Abstract
The world around us is complex, but at the same time full of meaningful regularities. We can detect, learn and exploit these regularities automatically in an unsupervised manner i.e. without any direct instruction or explicit reward. For example, we effortlessly estimate the average tallness of people in a room, or the boundaries between words in a language. These regularities and prior knowledge, once learned, can affect the way we acquire and interpret new information to build and update our internal model of the world for future decision-making processes. Despite the ubiquity of passively learning from the structured information in the environment, the mechanisms that support learning from real-world experience are largely unknown. By combing sophisticated cognitive tasks in human and rats, neuronal measurements and perturbations in rat and network modelling, we aim to build a multi-level description of how sensory history is utilised in inferring regularities in temporally extended tasks. In this talk, I will specifically focus on a rat model to study building and utilising prior knowledge in working memory behaviours.
The LIMLab is focused to study the mechanisms and neural principles by which the nervous system computes, represents and integrates various forms of sensory memories and priors in the process of learning and inferring meaningful statistical patterns and abstract relations in the environment.