Learning and inference in the brain
Learning and inference in the brain
复制标题
DOI：
10.1016/j.neunet.2003.06.005
复制
发表时间：
2003-11-01
期刊：
影响因子：
7.8
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Friston, KJ
这篇文章是关于大脑如何挖掘其感官输入的数据。在过去的一个世纪里，经过仔细的解剖学和生理学研究，出现了一些功能性脑解剖学的架构原则。这些原则是根据表征学习来考虑的，看看它们是否可以在纯理论考虑的基础上先验地预测。我们首先回顾层次感觉皮质的组织，特别注意前向和后向连接之间的区别。然后，我们回顾了表征学习的各种方法，作为生成模型的特殊案例，从监督学习开始，以基于经验贝叶斯的学习结束。后者预测了许多特征，比如在真实的大脑中看到的分层皮质系统、普遍存在的自上而下的向后影响以及向前和向后连接之间的功能不对称。本文提出的关键点是：(i)分层生成模型使经验先验的学习成为可能，并避免了关于非分层模型中固有的感官输入原因的先验假设。这些假设对于基于信息理论和高效或稀疏编码的学习方案是必要的，但在分层上下文中不是必要的。关键是，可能在大脑中实现生成模型的解剖基础结构是分层的。此外，基于经验贝叶斯的学习可以以生物学上合理的方式进行。（ii）第二点是，如果产生输入的过程不能反转，或者反转不能参数化，则向后连接是必不可少的。由于这些过程涉及多对一映射，本质上是非线性和动态的，因此它们通常是不可逆转的。这加强了生成模型（即向后连接）的显式参数化，以提供识别，并表明向前架构本身不足以进行感知。（iii）最后，由反向连接介导的生成模型中的非线性要求这些连接具有调节性，以便较高皮层水平的表征可以相互作用以预测较低水平的反应。这对于经验证明的前向和后向连接的功能不对称非常重要。(C) 2003 Elsevier Ltd.版权所有。
This article is about how the brain data mines its sensory inputs. There are several architectural principles of functional brain anatomy that have emerged from careful anatomic and physiologic studies over the past century. These principles are considered in the light of representational learning to see if they could have been predicted a priori on the basis of purely theoretical considerations. We first review the organisation of hierarchical sensory cortices, paying special attention to the distinction between forward and backward connections. We then review various approaches to representational learning as special cases of generative models, starting with supervised learning and ending with learning based upon empirical Bayes. The latter predicts many features, such as a hierarchical cortical system, prevalent top-down backward influences and functional asymmetries between forward and backward connections that are seen in the real brain.The key points made in this article are: (i) hierarchical generative models enable the learning of empirical priors and eschew prior assumptions about the causes of sensory input that are inherent in non-hierarchical models. These assumptions are necessary for learning schemes based on information theory and efficient or sparse coding, but are not necessary in a hierarchical context. Critically, the anatomical infrastructure that may implement generative models in the brain is hierarchical. Furthermore, learning based on empirical Bayes can proceed in a biologically plausible way. (ii) The second point is that backward connections are essential if the processes generating inputs cannot be inverted, or the inversion cannot be parameterised. Because these processes involve many-to-one mappings, are non-linear and dynamic in nature, they are generally non-invertible. This enforces an explicit parameterisation of generative models (i.e. backward connections) to afford recognition and suggests that forward architectures, on their own, are not sufficient for perception. (iii) Finally, non-linearities in generative models, mediated by backward connections, require these connections to be modulatory, so that representations in higher cortical levels can interact to predict responses in lower levels. This is important in relation to functional asymmetries in forward and backward connections that have been demonstrated empirically. (C) 2003 Elsevier Ltd. All rights reserved.