Our research focus falls within the definition of Computational Neuroscience, i.e., an interdisciplinary field which draws on applied mathematics, physics and computer science to understand, describe and predict the nervous system and its pathologies. At the moment our research projects can be framed within three major lines:
Understanding the neural mechanisms used by neural populations to encode/decode sensory-motor information: We are currently using pattern recognition techniques to understand the role played by the different oscillations within the neural code. The use of pattern recognition allows understanding and mimicking the coding/decoding processes that are carried out by neural populations in a trial-by-trial basis.
Development and evaluation of techniques to non-invasively study the brain electromagnetic activity in healthy subjects and patients: A traditional research topic of the members of this group has been the design, evaluation and application of different inverse solutions. One important aspect of the new research lines is the development of robust inverse solution methods for the analysis of single trials rather than averages over stimuli repetitions.
Bayesian modeling of perception and action: How the brain deals with noise and uncertainty: To use sensory information efficiently to make judgments and guide action, the brain must represent and use information about uncertainty in its computations for perception and action. This leads to the Bayesian coding hypothesis: that the brain represents sensory information probabilistically, in the form of probability distributions.One of our aims is to test the Bayesian coding hypothesis experimentally, and so determine whether and how neurons code information about sensory uncertainty.
Geneva University Hospital
Email: Sara (dot) GonzalezAndino (at) hcuge (dot) ch