Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...
Cognitive decline and hallucinations are common and debilitating non-motor symptoms, usually occurring during later phases of Parkinson’s disease (PD). Minor hallucinations (MH) appear early in the disease course and have been suggested to predict cognitiv ...
The formation and storage of memories has been under deep investigation for several decades. Nevertheless, the precise contribution of each brain region involved in this process and the interplay between them across memory consolidation is still largely de ...
Memory formation and storage rely on multiple interconnected brain areas, the contribution of which varies during memory consolidation. The medial prefrontal cortex, in particular the prelimbic cortex (PL), was traditionally found to be involved in remote ...
Learning and memory rely on synaptic communication in which intracellular signals are transported to the nucleus to stimulate transcriptional activation. Memory induced transcriptional increases are accompanied by alterations to the epigenetic landscape an ...
Short-term synaptic plasticity and modulations of the presynaptic vesicle release rate are key components of many working memory models. At the same time, an increasing number of studies suggests a potential role of astrocytes in modulating higher cognitiv ...
Episodic autobiographical memories are characterized by a spatial context and an affective component. But how do affective and spatial aspects interact? Does affect modulate the way we encode the spatial context of events? We investigated how one element o ...
Long-term memory formation relies on synaptic plasticity, neuronal activity-dependent gene transcription, and epigenetic modifications. Multiple studies have shown that HDAC inhibitor (HDACi) treatments can enhance individual aspects of these processes and ...
In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high ...
