Principles behind neural representations in complex tasks

A grand aim of neuroscience is understanding how the brain represents knowledge. Knowledge is not just a piece of information, but a web of information and their relationships - knowing a family means knowing the people and how they relate to each other. The brain learns/represents these underlying structures (e.g. family tree), profiting from this knowledge when encountering new situations (understanding new families). Learning and generalising these web structures is fundamental to intelligent behaviour. My research provides principles for learning these web structures, showing that models imbued with such principles account for known neural representations and predict novel neural phenomena. In particular I am interested in how hippocampus plays a role both in spatial cognition and higher level cognitive functions. Are the same principles at play for both cases? Can we build models of higher level cognitive tasks? I investigate these questions with theory, models, and data analysis.