Alla Borisyuk

The paper uses deep reinforcement learning to model risk-adaptive decision-making strategies and identifies low-dimensional neural dynamics associated with high-performing strategies in humans.

This study shows that an asymmetric reinforcement learning model better explains human decision-making under risk than symmetric models.

This paper develops an effective astrocyte model to study how astrocytic ensheathment influences neuronal network synchronization and collective dynamics.

This study shows how specific connectivity patterns in a Drosophila mushroom body model influence olfactory learning and odor representation.

The paper uses a computational model to study how interglomerular inhibitory networks in the mouse olfactory bulb shape odor representations based on connectivity schemes and sensory input.