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Causes and consequences of representational drift.

Michael E Rule1, Timothy O'Leary1, Christopher D Harvey2

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Summary
This summary is machine-generated.

Neural representations of learned tasks change over time, a phenomenon called representational drift. This drift allows the brain to adapt while maintaining stable behavior and consistent neural codes.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • The nervous system must balance learning new information with retaining existing memories.
  • Neuronal representations of sensorimotor tasks change continuously, even after expert performance is achieved.

Purpose of the Study:

  • To explore how learned information is stored despite ongoing changes in neuronal activity.
  • To understand the functional roles of representational drift in sensorimotor systems.

Main Methods:

  • Review of experimental evidence for representational drift.
  • Discussion within a framework of distributed population codes.
  • Integration of theoretical work on computational roles of drift.

Main Results:

  • Representational drift is observed in sensorimotor systems.
  • Distributed population codes allow for drift while maintaining behavioral consistency.
  • Recurrent network structures may limit disruptive effects of drift.

Conclusions:

  • Representational drift may generate error signals for maintaining consistent neural codes.
  • This framework offers new experimental and theoretical avenues for studying adaptive neural codes.