Efficient and adaptive sensory codes

Detecting stimulus changes requires different strategies than encoding stationary stimuli. Mlynarski and Hermundstad extend efficient coding theory to non-stationary environments and derive adaptive codes that best balance these competing objectives. The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.

Automated summary

The study explores the performance of adaptive sensory coding in response to environmental changes, revealing that optimized coding may underperform when the environment changes. To address this issue, sensory systems must balance the ability to accurately encode stimuli with the capacity to quickly adapt. The research provides a unified perspective on adaptive coding across various sensory systems, environments, and sensory tasks.