Research

My current scientific research is concerned with the study of neural systems by means of mathematical models. The usual way to carry out these studies is by employing numerical simulations of a large number of interconnected neurons, although analytical approaches are sometimes possible. The most common mathematical tools that I employ in these approaches come from Statistical Mechanics (mainly mean-field techniques), as well as from Dynamical Systems and Stochastic Processes Theory.

I pursued my PhD in the group of Statistical Physics in the University of Granada, Spain, under the supervision of Prof. Joaquín J. Torres. My research within this group focused on the influence of short-term synaptic dynamics in several tasks, such as signal detection, dynamics of population electrical activity, learning and memory. Some of my results from these studies suggest that the interplay between different synaptic mechanisms, such as short-term depression and facilitation, may be highly relevant in the processing and coding of information by neural ensembles. Moreover, the modelling of the competition between these synaptic mechanisms offers predictions which may be tested in in vitro or in vivo experiments.

In 2010, I moved to work as a postdoctoral researcher in the Centre for Neural Dynamics in the University of Ottawa, Canada, and more precisely in the laboratory of Prof. André Longtin. There I applied my knowledge on neural modelling to study redundant signal cancellation and novel stimuli detection. In order to achieve a deep understanding of these phenomena, we combined theoretical modelling and computer simulations with in vitro and in vivo electrophysiological recordings from the weakly electric fish, recorded in Prof. Len Maler's lab. I also worked in the effects of neural heterogeneity on neural coding properties.

In 2013 I moved to the Center for Neural Science in New York University to work, first as a postdoctoral associate and recently as an assistant research scientist, in the lab of Prof. Xiao-Jing Wang. While maintaining close collaborations with experimentalists, my main research focus is to develop data-driven large-scale models of the mammalian cortex and use them to study high cognitive functions, such as working memory, attention or decision making.

As a long-term goal, I plan to combine these large-scale models of the brain with my expertise on novelty detection and sensory systems, in order to develop functional large-scale models able to react efficiently to naturalistic stimuli.

For more details, see my list of publications.

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