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Neuroplasticity

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Related Experiment Videos

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.

Arthur Prat-Carrabin1, Maximilian V Harl2,3, Samuel J Gershman4

  • 1Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA. arthurpc@fas.harvard.edu.

Nature Communications
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Neural sensory systems adapt tuning curves to changing environments. A new model explains how gain modulation in recurrent networks achieves rapid adaptation, reconciling adapter repulsion and prior attraction phenomena.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sensory Processing

Background:

  • Neural sensory systems must adapt to changing environmental statistics to maintain accurate representations.
  • Neuronal tuning curves are hypothesized to optimize to the prior stimulus distribution under efficient coding principles.
  • Empirical observations of 'adapter repulsion' in tuning curves contrast with theoretical 'prior attraction' predictions, leaving the underlying mechanism unclear.

Purpose of the Study:

  • To investigate the mechanisms of rapid neural adaptation in sensory systems.
  • To reconcile the contrasting phenomena of adapter repulsion and prior attraction.
  • To propose a unified theoretical and mechanistic model for efficient coding in recurrent sensory networks.

Main Methods:

  • Development of a gain-adaptive, recurrent sensory network model.
  • Incorporation of an efficient coding objective balancing accuracy and spiking cost.
  • Analysis of adaptive tuning curve dynamics under varying prior distributions.

Main Results:

  • The model demonstrates that modulated gains propagating through the network lead to quickly adaptive tuning curves.
  • The model successfully accounts for adapter repulsion under peaked prior distributions.
  • The model predicts and is supported by behavioral evidence for fast prior attraction under broader distributions.

Conclusions:

  • Gain modulation in recurrent circuits provides a unified mechanism for rapid adaptation in neural sensory systems.
  • The proposed framework reconciles seemingly contradictory adaptive phenomena observed in neural tuning curves.
  • This work advances our understanding of efficient coding principles and their implementation in biological neural networks.