We are currently developing a deep neural network to predict neutrino trajectories in single-line events of the ANTARES telescope
This line of research utilizes a rule-based decision task and combines experimental procedures (psychophysics and neuroimaging) with computational approaches (reinforcement learning and neural circuit modeling) to encompass the dynamics, rise and decline of goal-directed behavior in humans...
Meta-reinforcement learning refers to embedding meta-learning (i.e., higher-order learning) mechanisms in reinforcement learning (RL) models. I have used this approach to significantly improve a repertoire of RL models...
Cortical neurons in sensory areas show patterns of irregular firing that are, however, synchronized to, e.g., gamma oscillations. This regime of noisy oscillations is further reinforced by selective attention, which arises questions such as: why do regular and irregular components appear simultaneously? are oscillations just reducing noise? Our results show that this regime has a more insteresting functionality...
Unlike Biased Competition, Unbiased Competition is resolved internally, i.e., in the absence of external biases. In Ardid et al. PNAS (2019), I identified how dissimilarities in the physiology of competing neural populations determine the direction of the resulting bias according to the characteristics of (unbiased) external inputs. There are three general ways in which Unbiased Competition may occur...