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

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This Nature Communications paper proposes a gain-adaptive recurrent sensory network that reconciles fast sensory adaptation with efficient-coding theory.

The model balances representational accuracy and spiking cost through gain modulation, producing adaptive tuning behavior without requiring slow synaptic rewiring.

It is relevant to AI because it gives a mechanistic account of rapid context-sensitive representation adjustment in recurrent networks, a useful conceptual primitive for adaptive perception systems.

For the neuroscience track, it clears the bar by linking a brain coding principle to a computational network model with direct representational-learning spillover.

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