@phdthesis{17346,
  abstract     = {Acquiring, retaining, and retrieving information over a wide range of timescales are crucial
functions of the brain. The successful processing of memories affects many aspects of our
lives and enables us and many other organisms to operate in a complex environment and
to interact with it. In this context, the hippocampus and functionally connected brain
areas, such as the prefrontal cortex, are central and have been subject to intensive research
in the past decades. Storage of memories is believed to rely on distributed neural activity
within these neural circuits. Additionally, neural memory traces of recent experience are
reinstated during periods of rest or sleep. These reactivations are thought to play an
outstanding role in the consolidation of memories and potentially facilitate the transfer of
information from the hippocampus to cortical areas for long-term storage and integration
into existing knowledge.
However, there is growing evidence that memory-related neural representations in the
hippocampus are not as stable as initially thought and that they change even in the
absence of learning. It has been suggested that these changes reflect the accumulation of
experience, but the influence of interspersed consolidation periods has not been considered.
Previous studies have analyzed consolidation periods by detecting activity that strongly
resembled neural activity during the acquisition of memory. Besides being often limited
to only non-rapid eye movement (NREM) sleep, the used approaches were not capable of
tracking changes in neural representations over extended temporal periods. More fluid
representations do not only challenge our understanding of how information is stored, but
they also affect the transfer of information between brain areas during the consolidation
process.
For this thesis, I investigated the evolution of memory-related activity during sleep
periods expected to be involved in consolidation in the hippocampus and between the
hippocampus and prefrontal cortex. I found that reactivated activity in the hippocampus
gradually transformed during prolonged periods of sleep and inactivity. In the beginning,
neural activity strongly resembled acquisition activity, whereas, with the progression of
time, it became more similar to the subsequent recall activity. NREM periods drove
this process, while rapid-eye movement (REM) periods showed a resetting effect. This
reactivation drift was due to firing rate changes of a subset of cells and mirrored the
representational changes from the acquisition to the recall. A stable subset of cells
withstood the drift and maintained their activity. Therefore, my results indicate that
memory-related representations undergo spontaneous modifications during consolidation
periods and that these changes are predictive of representational drift.
Furthermore, I found that the amount of change in the neural activity during subsequent
sleep periods was biased by prior behavioral performance. Observed changes in the
hippocampus and the prefrontal cortex were synchronized and increased after poor
performance, highlighting a potential role in the exchange of information. Low-variance
vii
periods with distinct, more stable activity from a subset of cells significantly contributed
to the heightened synchrony between both areas. Hence, interleaved phases of more stable
neural activity could facilitate the information transfer between brain areas.
In conclusion, my investigations underline the fluidity of memory-related representations
and assign a prominent role to sleep reactivation periods in their evolution. In addition, I
identified a potential mechanism of stable activity phases that might facilitate the synchronization across hippocampal-prefrontal activity despite ongoing changes. Reconciling
and integrating findings from both spontaneous and behaviorally-related representational
changes in functionally related brain areas will help to broaden our understanding of how
knowledge is stored, maintained, updated, and transferred between brain areas.},
  author       = {Bollmann, Lars},
  issn         = {2663-337X},
  keywords     = {Memory, Hippocampus, Consolidation},
  pages        = {103},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Stability and change in the memory system during rest}},
  doi          = {10.15479/at:ista:17346},
  year         = {2024},
}

@inproceedings{10004,
  abstract     = {Markov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decision processes (MDPs) extend Markov chains by incorporating non-deterministic behaviors. Given an MDP and rewards on states, a classical optimization criterion is the maximal expected total reward where the MDP stops after T steps, which can be computed by a simple dynamic programming algorithm. We consider a natural generalization of the problem where the stopping times can be chosen according to a probability distribution, such that the expected stopping time is T, to optimize the expected total reward. Quite surprisingly we establish inter-reducibility of the expected stopping-time problem for Markov chains with the Positivity problem (which is related to the well-known Skolem problem), for which establishing either decidability or undecidability would be a major breakthrough. Given the hardness of the exact problem, we consider the approximate version of the problem: we show that it can be solved in exponential time for Markov chains and in exponential space for MDPs.},
  author       = {Chatterjee, Krishnendu and Doyen, Laurent},
  booktitle    = {Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science},
  isbn         = {978-1-6654-4896-3},
  issn         = {1043-6871},
  keywords     = {Computer science, Heuristic algorithms, Memory management, Automata, Markov processes, Probability distribution, Complexity theory},
  location     = {Rome, Italy},
  pages        = {1--13},
  publisher    = {IEEE},
  title        = {{Stochastic processes with expected stopping time}},
  doi          = {10.1109/LICS52264.2021.9470595},
  year         = {2021},
}