A theory of cortical responses
A theory of cortical responses
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DOI：
10.1098/rstb.2005.1622
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发表时间：
2005-04-29
影响因子：
6.3
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Friston, KJ
这篇文章涉及诱发脑反应的性质和产生的原则。我们从这样一个前提开始，即感觉脑已经进化到能够代表或推断其感觉输入变化的原因。推理问题在统计学术语中有很好的表述。因此，推理的统计基础可能对神经元的实现提供重要的约束。根据现代统计理论，通过阐述亥姆霍兹关于知觉的原始思想，人们得出了一个可以解释大量神经生物学事实的知觉推理和学习模型，事实证明，推断感觉输入的原因（知觉推理）和学习输入与原因之间的关系（知觉学习）的问题可以用完全相同的原理来解决。具体来说，推理和学习都依赖于最小化大脑的自由能，正如统计物理学所定义的那样。此外，推理和学习可以以生物学上合理的方式进行。皮质反应可以被看作是大脑试图最小化由刺激引起的自由能，从而编码该刺激的最可能原因。类似地，学习出现在突触功效的变化中，其最小化自由能，对所遇到的所有刺激进行平均。潜在的计划依赖于经验贝叶斯和层次模型的感觉输入是如何引起的。分层模型的使用使大脑能够以动态和上下文敏感的方式构建先前的期望。这个方案提供了一个原则性的方法来理解皮层组织和反应的许多方面。本文的目的是在一个单一的理论视角下，涵盖大脑的许多明显无关的解剖学、生理学和心理物理学属性。在皮层结构方面，理论处理预测感觉皮层应该是分层排列的，连接应该是相互的，向前和向后的连接应该表现出功能不对称性（前向连接是驱动性的，而后向连接既是驱动性的又是调节性的）。在突触生理学方面，它预测了联想可塑性，并为动态模型，尖峰时间依赖的可塑性。在电生理学方面，它解释了经典和超经典的感受野效应以及诱发皮层反应的长潜伏期或内源性成分。它预测了感知学习编码预测误差的反应衰减，并解释了许多现象，如重复抑制，失配负波（MMN）和P300在脑电图。在心理物理学领域，它解释了这些生理现象的行为相关性，例如，启动和全局优先。本文的最后一个重点是感知学习与MMN测量和皮质区之间的耦合使用诱发的感觉反应的实证研究的影响。
This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus' and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective.In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical tern-is, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.