Athena Akrami: Learning and exploiting sensory statistical structures with and without feedback

Abstract

A defining feature of animal intelligence is the ability to discover and update knowledge of statistical regularities in the sensory environment, in service of adaptive behaviour. This allows animals to build appropriate priors, in order to disambiguate noisy inputs, make predictions and act more efficiently. Despite decades of research in the field of human cognition and theoretical neuroscience, it is not known how such learning can be implemented in the brain. By combing sophisticated cognitive tasks in humans, rats, and mice, as well as neuronal measurements and perturbations in the rodent brain and computational modelling, we seek to build a multi-level description of how sensory history is utilised in inferring regularities in temporally extended tasks. In this talk, I will specifically focus on a cross-species model to study statistical learning, in both feedback-based and non-feedback-based settings.

Duration of the talk:

Approx. 50 minutes, then general and specialized discussion.

Where:

FIT Seminar Room, Georges-Köhler-Allee 105, ground floor

More about the speaker and his research

Athena Akrami

Host

Ilka Diester