RI: Small: A Hierarchical Approach to Unsupervised Feature Discovery

与流行媒体的一些警告相反，人类在理解周围的视觉世界方面仍然远远优于任何计算机程序。如果我们了解视觉系统是如何做到这一点的，也许可以建立更好的人工视觉系统。这个项目的目标是了解大脑如何代表视觉世界以及为什么。理查德·费曼（Richard Feynman）曾说过一句名言：“我不能创造什么，我就不明白什么”，这个项目的方法是创建一个从自然输入（图像，视频）中学习的计算机系统，假设视觉系统的目标是有效地表示世界。然后将这些表示与视觉神经元的测量进行比较。长期目标是了解早期视觉处理层在人类视觉通路中的功能作用，该模型基于有效编码假设，即早期视觉通路通过有效编码视觉信息来捕获其视觉输入的统计结构。大多数遵循这一假设的计算模型只关注一个或两个视觉层的建模。在这个项目中，Cottrell的团队提出了一个分层信息处理模型，该模型符合有效编码假设，但提供了迄今为止对早期视觉处理层的最完整描述。在该模型中，首先使用稀疏主成分分析（SPCA）压缩视觉输入以降低噪声，然后使用过完备稀疏编码扩展数据维度以捕获视觉输入的统计结构。然后，一个非线性激活函数将这一层的输出格式化为下一层，整个过程重复进行。初步工作表明，所得到的分层模型可以学习视觉功能，表现出早期视觉通路中神经元的感受野特性，包括视网膜神经节细胞，LGN，V1简单和复杂细胞，以及V2细胞。

Contrary to some depictions in popular media, humans are still far better than any computer program at understanding the visual world around them. If we understood how the visual system does this, perhaps better artificial vision systems could be built. The goal of this project is to understand how the brain represents the visual world and why. Following a mantra famously credited to Richard Feynman -- What I cannot create, I do not understand -- this project's approach is to create a computer system that learns from natural input (images, videos), assuming that the visual system operates with the goal of efficiently representing the world. These representations will then be compared to measurements of visual neurons. The long term goal is to understand the functional roles of the early visual processing layers in the human visual pathway.The model is based on the efficient coding hypothesis, in which the early visual pathway serves to capture the statistical structure of its visual inputs by efficiently coding visual information in its outputs. Most computational models following this hypothesis have focused on modeling only one or two visual layers. In this project, Cottrell's group proposes a hierarchical information processing model, which concurs with the efficient coding hypothesis, yet provides the most complete description so far of the early visual processing layers. In this model, the visual inputs are first compressed to reduce noise using Sparse Principal Components Analysis (SPCA), then the data dimensions are expanded to capture the statistical structure of the visual inputs using overcomplete Sparse Coding. A nonlinear activation function then formats the outputs of this layer for the next layer up, and the whole process is repeated. Preliminary work shows that the resulting hierarchical model can learn visual features exhibiting the receptive field properties of neurons in the early visual pathway, including retinal ganglion cells, LGN, V1 simple and complex cells, and V2 cells.