Institute of Science and Technology Austria, Klosterneuburg, Austria.
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
Nat Neurosci. 2021 Jul;24(7):998-1009. doi: 10.1038/s41593-021-00846-0. Epub 2021 May 20.
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.
适应刺激统计变化的能力是感觉系统的一个标志。在这里，我们从信息处理的角度开发了一个可以解释适应动力学的理论框架。我们使用这个框架来优化和分析自适应感觉代码，并表明在环境发生变化时，针对静止环境优化的代码可能会出现长时间性能不佳的情况。为了减轻这些环境变化的不利影响，感觉系统必须在准确编码传入刺激的能力和快速检测和适应这些刺激分布变化的能力之间进行权衡。我们推导出了一系列平衡这些目标的代码，并证明它们与均值和方差适应过程中观察到的神经动力学非常匹配。我们的研究结果为各种感觉系统、环境和感觉任务提供了一个统一的适应视角。
