There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
The nervous system learns new associations while maintaining memories over long periods,
exhibiting a balance between flexibility and stability. Recent experiments reveal
that neuronal representations of learned sensorimotor tasks continually change over
days and weeks, even after animals have achieved expert behavioral performance. How
is learned information stored to allow consistent behavior despite ongoing changes
in neuronal activity? What functions could ongoing reconfiguration serve? We highlight
recent experimental evidence for such representational drift in sensorimotor systems,
and discuss how this fits into a framework of distributed population codes. We identify
recent theoretical work that suggests computational roles for drift and argue that
the recurrent and distributed nature of sensorimotor representations permits drift
while limiting disruptive effects. We propose that representational drift may create
error signals between interconnected brain regions that can be used to keep neural
codes consistent in the presence of continual change. These concepts suggest experimental
and theoretical approaches to studying both learning and maintenance of distributed
and adaptive population codes.