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Drifting neuronal representations: Bug or feature?

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Neural representations in the brain change over time, a phenomenon called representational drift. This review explores how these unstable neural codes support stable memories and behaviors.

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Area of Science:

  • Neuroscience
  • Cognitive Science

Background:

  • The brain sustains stable memories essential for behavior and stimulus association.
  • Recent findings show single-neuron representations continuously change, challenging static feature assumptions.

Purpose of the Study:

  • To review evidence for representational drift across brain areas.
  • To dissect the functional characteristics and mechanisms of representational drift.
  • To explore theoretical proposals for the computational benefits of drift.

Main Methods:

  • Review of recent experimental evidence on neural coding.
  • Analysis of functional characteristics and underlying mechanisms of representational drift.
  • Examination of theoretical models of neural computation.

Main Results:

  • Representational drift is observed across various brain areas.
  • Drift is not merely noise but can arise from beneficial computational mechanisms.
  • Hierarchical networks performing probabilistic computations may generate drift.

Conclusions:

  • Unstable neural codes can support robust perception, memory, and actions.
  • Representational drift may be an emergent property of efficient neural computation.
  • Understanding drift offers insights into brain function and memory stability.