Computational Neurobiology Laboratory, Salk Institute for Biological Studies, United States.
Computational Neurobiology Laboratory, Salk Institute for Biological Studies, United States; Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, United States.
Curr Opin Neurobiol. 2014 Apr;25:47-53. doi: 10.1016/j.conb.2013.11.007. Epub 2013 Dec 14.
The ability to make accurate predictions of future stimuli and consequences of one's actions are crucial for the survival and appropriate decision-making. These predictions are constantly being made at different levels of the nervous system. This is evidenced by adaptation to stimulus parameters in sensory coding, and in learning of an up-to-date model of the environment at the behavioral level. This review will discuss recent findings that actions of neurons and animals are selected based on detailed stimulus history in such a way as to maximize information for achieving the task at hand. Information maximization dictates not only how sensory coding should adapt to various statistical aspects of stimuli, but also that reward function should adapt to match the predictive information from past to future.
对未来刺激和自身行为结果进行准确预测的能力,对于生存和适当决策至关重要。这些预测在神经系统的不同层面上不断进行。这一点可以通过感觉编码中对刺激参数的适应,以及在行为层面上对环境最新模型的学习得到证明。本综述将讨论最近的发现,即神经元和动物的行为是根据详细的刺激历史来选择的,以便最大限度地获取完成手头任务所需的信息。信息最大化不仅决定了感觉编码应该如何适应刺激的各种统计方面,还决定了奖励功能应该如何适应,以匹配过去到未来的预测信息。