Replay in Biological and Artificial Neural Networks

The brain constantly forms memories of the places, people and events we experience. An area of the
brain called the hippocampus is especially important for this process - without it we cannot form
memories of our experiences. However, exactly how the brain creates and stores these memories
remains unknown. Research has shown that memories are stored as distributed neural codes: activity
in specific neurons corresponds to specific places or information. However, the mechanism that
stabilises these neural codes for long term storage is unclear. One possibility is the reactivation, or
'replay', of hippocampal neural codes during rest. This replay is thought to strengthen connections
between neurons encoding the memory in both the hippocampus and distributed neocortical regions.
However, the impact of such memory replay on cortical neural circuits is unclear. The first part of my
project will address how hippocampal replay impacts physiological mechanisms that stabilise cortical
memories. A recent study claims to have recorded replay in the human hippocampus using functional
magnetic resonance imaging (fMRI) - an indirect, non-invasive method for inferring brain activity. Our
study will build on this approach to use fMRI to investigate the relationship between hippocampal
replay and memory storage in the human neocortex. The second part of my project will test theories
that replay facilitates learning of new information from past experience. It has been suggested that
the learnt or abstracted information can be used to solve problems more efficiently. We will test this
hypothesis by again using fMRI to measure replay in the human hippocampus. Incorporating memory
architectures, such as replay, into machine learning systems has previously improved their ability to
solve tasks and analyse data. The final part of the project aims to apply experimental findings on
replay to computational learning systems.