Topic: recurrent neuronal networks

Mammalian brains increasingly reuse the same neurons and regions for different tasks, while maintaining robust performance when the environment or stimulus context changes. Udo Ernst combines theoretical modelling with quantitative physiological observations to investigate the mechanisms that support both flexibility and resilience. Selective processing in the primate visual system provides the central example. One analytically tractable framework routes attended external signals through avalanches of spikes in recurrent neuronal networks; a second examines control circuits that preserve relia

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map e