I am a Research Scientist at the Theoretical anc Cognitive Neuroscience Group (CNS), at the Center for Brain and Cognition (CBC) since July 2018.
My main research interest is on the motor control aspects of decision-making. Specifically, I am devoted to elucidate the brain mechanisms underlying decisions between motor actions, and the principles operating the selection of movement parameters as a function of sensory information, of the structure the motor apparatus and of motivation. In neuroscience, one of the most relevant questions is how the brain encodes actions implying different costs and yielding different payoffs, each associated to a different option, to finally decide a movement of specific kinematic and dynamic properties. Understanding how this operates has both an academic and a clinical interest, to devise the principles underlying the generation of specific movements, and to provide a more comprehensive understanding of volitional disorders such as Parkinson's Disease. My approach consists of a combination of experimental techniques (Psychophysics and Transcranial Magnetic Stimulation) to investigate the manner in which movement is generated under specific experimental conditions and to probe the operation of specific brain areas, and of computational techniques (Theoretical Models at different levels of description) to investigate the dynamics of the cortex and the basal ganglia during the process of defining the parameters to elicit specific movements. I am actively collaborating with Paul Cisek at the University of Montreal.
Specifically, my main project focuses on understanding how the brain controls variability as a function of the person's motivational state. The project is directed by Prof. Gustavo Deco at the Center for Brain and Cognition in Barcelona.
List of TFG/TFM offered for 2019/2020 :
- RLMov: Learning the Structure of Movement by Reinforcement
- Parkinson’s Disease Brain States and Functional Connectivity: A Machine Learning Analysis of Neuroimaging Data
- Brain Functional Connectivity of Motivated States: A Machine Learning Analysis
- Machine Learning Identification of Functional Connectivity with iEEG
- From the Visual Analysis of Movement to Principled Models of Motivated Movement