My Lab is interested in identifying physiological mechanisms of brain function and dysfunction, in particular, the neural substrates of learning and intelligence. For this, we build neural network and reinforcement learning models.
While keeping a strong interest in computational neuroscience, new endeavors seek to adapt computationally efficient mechanisms of the brain into AI architectures (such as deep neural networks) aiming for more powerful learning algorithms.
Before leading the Lab of Natural and Designed Intelligence at UPV, I was an Associate Research Scientist at Yale, where I was part of the Decision Lab (led by my current collaborator Ifat Levy). As a Postdoctoral Research Scientist, I collaborated with Nancy Kopell (Boston University), Thilo Womelsdorf (Vanderbilt University), and Xiao-Jing Wang (New York University). I earned my PhD in Computational Neuroscience at the Neuroscience Institute (UMH-CSIC), under the guidance of Albert Compte (IDIBAPS – Hospital Clinic).
Habilitation as Professor Lector (Tenure-Track Assistant Professor), 2019
AQU, Catalonia
Habilitation in Neuroscience as Maître de Conférences (Tenure-Track Assistant Professor), 2017
Conseil National des Universités, France
PhD in Computational Neuroscience, 2008
Neuroscience Institute, UMH-CSIC
MEng in Electronic Engineering, 2002
Universitat de València
BSc & MSc in Physics, 1999
Universitat de València
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…
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…