My project focuses on developing new methodologies for quantifying interactions between neural populations from electrophysiology data.

Sensory systems are typically considered as hierarchically organized feed forward streams, such as the retino-geniculate pathway and its follow-up ventral and dorsal streams that pass information from thalamus to a large number of cortical areas. Yet, connectivity in cortex is strictly bidirectional, and for each pair of interconnected cortical regions there is also a parallel cortical-thalamo-cortical path. At a smaller spatial scale, the neural tissue is locally organized in microcircuits of strongly interconnected populations of inhibitory and excitatory neurons.

Understanding how information is processed in this highly recurrent brain network thus requires a fine quantification of its functional and causal interactions. To achieve this, I am developing new statistical analysis techniques for neurophysiology data. In particular, I use information theory and kernel methods to detect causal dependencies in experimental time series. I am currently applying this techniques to multivariate recordings of extracellular potentials in the monkey visual cortex to better understand local processing in cortical microcircuits and communication between distinct visual areas.