Continual task learning in natural and artificial agents
It addresses the problem of continual learning in neuroscience and AI, but is incremental as it synthesizes existing research without new experimental results.
The paper reviews recent brain recording studies and computational models to understand how neural representations change during task learning, focusing on minimizing interference between tasks.
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.