Prism Adaptation (PA) is a useful method to study the mechanisms of sensorimotor adaptation. After-effects following adaptation to the prismatic deviation constitute the probe that adaptive mechanisms occurred, and current evidence suggests an involvement ...
Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) ...
Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal rep ...
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 ...
decrement have been proposed, such as weakened acquisition of the motor skill. While the processes at play during the initial acquisition phase have been well-characterized in young adults, they were only scarcely investigated in older adults. The goal of ...
Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...
Neural computational power is determined by neuroenergetics, but how and which energy substrates are allocated to various forms of memory engram is unclear. To solve this question, we asked whether neuronal fueling by glucose or lactate scales differently ...
We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...
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 ...
