Grzywacz Norberto M, de Juan Joaquín
Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, University Park, OHE 500, 3650 S McClintock Avenue, Los Angeles, CA 90089-1451, USA.
Network. 2003 Aug;14(3):465-82.
Sensory adaptation allows biological systems to adjust to variations in the environment. A recent theoretical work postulated that the goal of adaptation is to minimize errors in the performance of particular tasks. The proposed minimization was Bayesian and required prior knowledge of the environment and of the limitations of the mechanisms processing the information. One problem with that formulation is that the environment changes in time and the theory did not specify how to know what the current state of the environment is. Here, we extend that theory to estimate optimally the environmental state from the temporal stream of responses. We show that such optimal estimation is a generalized form of Kalman filtering. An application of this new Kalman-filtering framework is worked out for retinal contrast adaptation. It is shown that this application can account for surprising features of the data. For example, it accounts for the differences in responses to increases and decreases of mean contrasts in the environment. In addition, it accounts for the two-phase decay of contrast gain when the mean contrast in the environment rises suddenly. The success of this and related theories suggest that sensory adaptation is a form of constrained biological optimization.
感觉适应使生物系统能够适应环境变化。最近的一项理论研究假定，适应的目标是将特定任务执行中的误差降至最低。所提出的最小化是贝叶斯式的，需要有关环境以及处理信息机制局限性的先验知识。该公式的一个问题是环境随时间变化，而该理论并未具体说明如何知晓环境的当前状态。在此，我们扩展该理论，以便从响应的时间流中最优地估计环境状态。我们表明，这种最优估计是卡尔曼滤波的一种广义形式。针对视网膜对比度适应，给出了这个新的卡尔曼滤波框架的一个应用。结果表明，该应用能够解释数据中令人惊讶的特征。例如，它解释了对环境中平均对比度增加和减少的响应差异。此外，它还解释了环境中平均对比度突然上升时对比度增益的两阶段衰减。这一理论及相关理论的成功表明，感觉适应是一种受约束的生物优化形式。
