Daniel Haşegan

The paper examines Mapper parameter choices in functional neuroimaging data to improve the extraction of topological and temporal features using synthetic and real fMRI data.

This paper trains spiking neuronal network models to play a virtual racket-ball game using reinforcement learning and evaluates different circuit architectures and learning rules.

This study compares spike-timing-dependent reinforcement learning and evolutionary strategy for training spiking neuronal networks to solve a reinforcement learning control task.