FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
Eur J Neurosci. 2012 Apr;35(7):1169-79. doi: 10.1111/j.1460-9568.2012.08010.x.
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and brain function. Firstly, we review some key characteristics of Bayesian systems - they integrate information making rational use of uncertainty, they apply prior knowledge in the interpretation of new observations, and (for several reasons) they are very effective learners. Secondly, we illustrate how some well-known psychological phenomena including visual illusions, categorical perception and attention can be understood in terms of Bayesian inference. We also consider how formal models can clarify our understanding of psychological constructs, by giving a truly computational definition of psychological processes. Finally, we consider how probabilistic representations and hence Bayesian algorithms could be implemented by neural populations. In particular, we explore how different types of population coding may lead to different predictions about activity in both single-unit and imaging studies, and draw a distinction in this context between the representation of parameters and implementation of computations.
在这篇综述中，我们探讨了贝叶斯逻辑如何帮助神经科学家理解行为和大脑功能。首先，我们回顾了贝叶斯系统的一些关键特征——它们整合信息，合理利用不确定性，在解释新观察结果时应用先验知识，并且（由于几个原因）它们是非常有效的学习者。其次，我们举例说明了一些众所周知的心理现象，包括视觉错觉、范畴知觉和注意力，如何根据贝叶斯推理来理解。我们还考虑了如何通过为心理过程提供真正的计算定义，使形式模型能够澄清我们对心理结构的理解。最后，我们考虑了概率表示形式，以及因此贝叶斯算法如何通过神经群体来实现。特别是，我们探讨了不同类型的群体编码如何导致对单细胞和成像研究中活动的不同预测，并在此背景下区分参数表示和计算实现。
