Computational Neuroscience
My research work is at the interface between systems neuroscience and cellular neurophysiology. The goal is to uncover the principles of cortical functions in terms of cellular mechanisms, network connectivity, and large-scale population dynamics. This is accomplished through theoretical modeling and analysis of biophysically realistic models, in connection with experimental findings. I also use techniques from statistical physics and nonlinear dynamics to gain further insights into the behavior of neural systems. Currently, I am working on neural computation and memory in cortical circuits. I am studying how animals and humans make simple perceptual decisions and the neural mechanisms responsible for them. These can include selecting an action among competing alternative choices, or even controlling whether or not to make an action. These sensorimotor processes can be influenced not only by external stimuli, but also from factors such as stimulus or performance history, neuromodulation, reward and learning, and other ''top-down'' cognitive controls.
Physics
A challenging problem is the derivation of collective transport properties such as heat conduction and the understanding of the corresponding fluctuations. In this regard, large-deviation relationships are currently studied under the names of fluctuation and non-equilibrium work theorems. These large-deviation relationships play a fundamental role in characterizing the dynamical properties of non-equilibrium steady states. They account for the effect of large fluctuations and strong nonequilibrium constraints, which makes them suitable for applications in a variety of problems involving nanoscale systems, from materials science to biology. In this context, we use microscopic and stochastic approaches to further develop these relations and explore their consequences at the theoretical and experimental levels.
A major trend in fundamental sciences is the study of nonlinear systems and of the appearance of complex behavior. In this context, the understanding of information generation in complex systems is of particular interest. Nonequilibrium systems present a generic temporal and spatial order characterized, for instance, by the presence of long tails in the correlation functions. Our goal is to understand the origin, behavior, and constructive role of these phenomena.
Our work on copolymerization processes has shown that molecular information can only be stored and retrieved reliably away from equilibrium. The most celebrated example of such copolymerization processes is the replication of DNA by which genetic information is copied at the molecular level. Another situation of interest regards the physical limits to signaling in biological systems, where molecular noise limits the reliability of information retrieval and transmission. Ultimately these aspects must be linked to the architecture and nonlinear behaviors of the underlying genetic and chemical networks. In any case, the study of information processing's energetics may shed new lights on these fascinating problems.
The study of heat and electronic transport properties at the mesoscopic level is a topic of growing interest. For instance, new low-temperature devices are synthesized that involve the transfer of single electrons through the circuit. At this scale, new effects such as Coulomb blockade or quantized conductance come into play and shape the transport properties of these devices. We currently focus our investigation on higher-order current noise spectrum, which contains information on the transport properties in the nonlinear regime. More generally, we intend to further explore the new relations relating nonequilibrium fluctuations to nonlinear response we recently obtained, and explore their implications at the mesoscopic and macroscopic levels.