Replay in Biological and Artificial Neural Networks

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The brain constantly forms memories of the places, people and events we experience. An area of thebrain called the hippocampus is especially important for this process - without it we cannot formmemories of our experiences. However, exactly how the brain creates and stores these memoriesremains unknown. Research has shown that memories are stored as distributed neural codes: activityin specific neurons corresponds to specific places or information. However, the mechanism thatstabilises 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 connectionsbetween 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 myproject will address how hippocampal replay impacts physiological mechanisms that stabilise corticalmemories. A recent study claims to have recorded replay in the human hippocampus using functionalmagnetic resonance imaging (fMRI) - an indirect, non-invasive method for inferring brain activity. Ourstudy will build on this approach to use fMRI to investigate the relationship between hippocampalreplay and memory storage in the human neocortex. The second part of my project will test theoriesthat replay facilitates learning of new information from past experience. It has been suggested thatthe learnt or abstracted information can be used to solve problems more efficiently. We will test thishypothesis by again using fMRI to measure replay in the human hippocampus. Incorporating memoryarchitectures, such as replay, into machine learning systems has previously improved their ability tosolve tasks and analyse data. The final part of the project aims to apply experimental findings onreplay to computational learning systems.