Stefania Sarno (INMED - IRPHE)
Why animals learn the way they do? An experimentally driven computational approach
Team: David Robbe (INMED) - Christophe Eloy (IRPHE)
April 2019 - present | CENTURI postdoctoral fellow
2012 - 2017 | PhD in Biophysics, Universidad Autonoma de Madrid (Cantoblanco, Spain)
2007 - 2010 | MSc degree in Theoretical Physics - Universitá di Pisa (Pisa, Italy)
About her postdoctoral project
By constantly interacting with their environment, animals are capable of developing adaptive strategies to maximize reward collection, avoid punishments and minimize energy expenditure. The biological algorithms underlying trial-and-error learning are largely unknown. To address this question, we will examine whether different computational models can reproduce the learning dynamics and behavioral strategy of rats in a laboratory-based task. The data to model are already acquired and come from experiments in which animals, engaged in a multi-trial time estimation task, converged progressively towards a conserved embodied strategy. Learning models will be based on classical reinforcement techniques and more recent developments coming from artificial intelligence (deep learning). In this project, the back and forth interaction between experiments and theory will further the understanding of the mechanisms underlying learning and trial-by-trial adjustment in performance.