  How do humans and other animals learn new tasks? A wave of brain recording
studies has investigated how neural representations change during task
learning, with a focus on how tasks can be acquired and coded in ways that
minimise mutual interference. We review recent work that has explored the
geometry and dimensionality of neural task representations in neocortex, and
computational models that have exploited these findings to understand how the
brain may partition knowledge between tasks. We discuss how ideas from machine
learning, including those that combine supervised and unsupervised learning,
are helping neuroscientists understand how natural tasks are learned and coded
in biological brains.
