This thesis investigates how through experience the brain acquires and stores memories,
and uses these to extract and modify knowledge. This question is being studied
by both computational and experimental neuroscientists as it is of relevance for neuroscience,
but also for artificial systems that need to develop knowledge about the world
from limited, sequential data. It is widely assumed that new memories are initially
stored in the hippocampus, and later are slowly reorganised into distributed cortical
networks that represent knowledge. This memory reorganisation is called systems consolidation.
In recent years, experimental studies have revealed complex hippocampal-neocortical
interactions that have blurred the lines between the two memory systems,
challenging the traditional understanding of memory processes. In particular, the prior
existence of cortical knowledge frameworks (also known as schemas) was found to
speed up learning and consolidation, which seemingly is at odds with previous models
of systems consolidation. However, the underlying mechanisms of this effect are not
known.
In this work, we present a computational framework to explore potential interactions
between the hippocampus, the prefrontal cortex, and associative cortical areas
during learning as well as during sleep. To model the associative cortical areas, where
the memories are gradually consolidated, we have implemented an artificial neural network
(Restricted Boltzmann Machine) so as to get insight into potential neural mechanisms
of memory acquisition, recall, and consolidation.
We analyse the network’s properties using two tasks inspired by neuroscience experiments.
The network gradually built a semantic schema in the associative cortical
areas through the consolidation of multiple related memories, a process promoted by
hippocampal-driven replay during sleep. To explain the experimental data we suggest
that, as the neocortical schema develops, the prefrontal cortex extracts characteristics
shared across multiple memories. We call this information meta-schema. In our model,
the semantic schema and meta-schema in the neocortex are used to compute consistency,
conflict and novelty signals. We propose that the prefrontal cortex uses these
signals to modulate memory formation in the hippocampus during learning, which in
turn influences consolidation during sleep replay.
Together, these results provide theoretical framework to explain experimental findings
and produce predictions for hippocampal-neocortical interactions during learning
and systems consolidation
