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首页> 外文期刊>The European Journal of Neuroscience >How can a Bayesian approach inform neuroscience?
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How can a Bayesian approach inform neuroscience?

机译：贝叶斯方法如何告知神经科学？

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摘要

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.
机译：在这篇综述中，我们考虑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-它们整合了信息，合理地利用不确定性，将先验知识应用于新观测的解释中，并且（由于多种原因）它们是非常有效的学习者。其次，我们说明如何通过贝叶斯推理来理解一些众所周知的心理现象，包括视觉幻觉，分类感知和注意力。我们还考虑了形式模型如何通过给出心理过程的真实计算定义来阐明我们对心理建构的理解。最后，我们考虑如何通过神经群体来实现概率表示以及贝叶斯算法。特别是，我们探讨了不同类型的种群编码如何导致单单元和成像研究中有关活动的不同预测，并在此背景下区分了参数表示形式和计算实现方式。

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