Learning and exploiting sensory statistical structures with and without feedback

Athena Akrami, faculty at the Sainsbury Wellcome Centre, UCL/UK

TIME: Wednesday, October 15, 12:15 PM Amsterdam, Berlin, Rom, Stockholm, Wien

WHERE: Zoom https://zoom.us/j/93082049630?pwd=baF0hvvrKKcav7efF8y5oL0uPqrtIl.1
Meeting-ID: 930 8204 9630
Kenncode: BW-Guest

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.

BIO: Athena Akrami joined the faculty at the Sainsbury Wellcome Centre, UCL, in October 2018. She obtained her BA in Biomedical Engineering from Tehran Polytechnic (Amirkabir University of Technology) and her PhD in Computational Neuroscience from International School for Advanced Studies (SISSA, Trieste), with Alessandro Treves.  She was a postdoctoral fellow at SISSA where she worked with Mathew Diamond, and then at Princeton University where she was a Howard Hughes Medical Institute fellow and worked with Carlos Brody on Parametric Working Memory.

HOST: Ilka Diester

We look forward to welcoming you to our guest lecture online.