Bayesian brain : probabilistic approaches to neural coding
Bayesian brain : probabilistic approaches to neural coding
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DOI：
10.7551/mitpress/9780262042383.001.0001
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发表时间：
2006-12
期刊：
影响因子：
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通讯作者：
銅谷 賢治;石井 信;A. Pouget;Rajesh P. N. Rao
中科院分区：
文献类型：
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作者：
銅谷 賢治;石井 信;A. Pouget;Rajesh P. N. Rao
贝叶斯方法可以通过对理想的感觉系统应该如何结合先验知识和观察进行标准化预测，通过提供对大脑回路动态功能的机械性解释，以及通过建议破译实验数据的最佳方法，来帮助在多个层面上理解大脑。贝叶斯大脑汇集了实验和理论神经学家的贡献，他们根据贝叶斯估计的概念检查感知、决策和运动控制的大脑机制。在概述了理解所讨论方法的基本数学概念(包括贝叶斯定理)之后，贡献者讨论了如何使用贝叶斯概念来解释神经生物学数据，如神经棘波和脑功能成像。接下来，撰稿人研究了感觉处理的建模，包括关于外部世界的信息的神经编码。最后，贡献者探索适当行为的动态过程，包括知觉决策的速度和准确性的数学以及信念传播的神经模型。
A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering experimental data. Bayesian Brain brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation.After an overview of the mathematical concepts, including Bayes' theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging. Next, contributors examine the modeling of sensory processing, including the neural coding of information about the outside world. Finally, contributors explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.