Hierarchical Bayesian inference in the visual cortex
Hierarchical Bayesian inference in the visual cortex
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DOI：
10.1364/josaa.20.001434
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发表时间：
2003-07-01
影响因子：
1.9
通讯作者：
Mumford, D
中科院分区：
文献类型：
--
作者：
Lee, TS;Mumford, D
视觉处理的传统观点认为，V1和V2区域的早期视觉神经元是静态的时空滤波器，从视觉场景中提取局部特征。然后，提取的信息通过一系列在更高视觉区域的前馈模块链进行进一步分析。最近对清醒行为猴子的早期视觉神经元进行的电生理记录显示，早期视觉皮层的信息处理具有多个复杂层面，这从其神经元的长潜伏期反应中可以看出。这些新发现表明，早期视觉皮层的活动与视觉系统的其他部分紧密耦合且高度相互作用。它们促使我们基于分层贝叶斯推理的数学框架提出一种关于视觉系统推理的新理论设定。在这个框架中，皮层中的循环前馈/反馈回路用于整合自上而下的情境先验和自下而上的观察结果，以便沿着视觉层级实现并发的概率推理。我们认为粒子滤波和贝叶斯信念传播算法可能对这些相互作用的皮层计算进行建模。我们回顾了一些支持这些观点合理性的近期神经生理证据。（C）2003美国光学学会
Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas. (C) 2003 Optical Society of America.