Learning and adaptation, the organism’s ability of flexibly reacting and adjusting to its environment is central for brain functioning and behaviour. As such, learning has been broadly investigated under different labels in virtually all areas of neuroscience and cognitive science. Different approaches have been used; in particular, computational neuroscience has addressed the computational bases of learning, while neuroscientific research in animals including humans has explored the implementation of these computational principles in the nervous system. The essential challenge the brain faces is to allow for plasticity while keeping a certain level of stability. How to act reliably in a complex environment, while acknowledging and handling the fact that the environment itself will change over time?

The present symposium will concentrate on the latest developments on the issue of stability vs. plasticity on both computational and neural levels. Presentations will focus on demonstrating how the nervous system maintains the stability‐plasticity balance across different time scales, e.g. during performing a unimodal or crossmodal task or while acquiring knowledge such as object representations across the life span, as well as in response to injury such a sensory deprivation or disease. While presentations from neuroscience will introduce recent findings of how learning is implemented in the nervous system, computationally oriented presentation will discuss contemporary advances in traditional, dynamical and probabilistic frameworks for understanding the rationale why these neural mechanisms are efficient for keeping the stability‐plasticity balance in the brain and thus for guaranteeing adaptive behaviour.

Image: pixabay.com