The human visual system has a hierarchical structure consisting of layers of
processing, such as the retina, V1, V2, etc. Understanding the functional roles
of these visual processing layers would help to integrate the
psychophysiological and neurophysiological models into a consistent theory of
human vision, and would also provide insights to computer vision research. One
classical theory of the early visual pathway hypothesizes that it serves to
capture the statistical structure of the visual inputs by efficiently coding
the visual information in its outputs. Until recently, most computational
models following this theory have focused upon explaining the receptive field
properties of one or two visual layers. Recent work in deep networks has
eliminated this concern, however, there is till the retinal layer to consider.
Here we improve on a previously-described hierarchical model Recursive ICA
(RICA) [1] which starts with PCA, followed by a layer of sparse coding or ICA,
followed by a component-wise nonlinearity derived from considerations of the
variable distributions expected by ICA. This process is then repeated. In this
work, we improve on this model by using a new version of sparse PCA (sPCA),
which results in biologically-plausible receptive fields for both the sPCA and
ICA/sparse coding. When applied to natural image patches, our model learns
visual features exhibiting the receptive field properties of retinal ganglion
cells/lateral geniculate nucleus (LGN) cells, V1 simple cells, V1 complex
cells, and V2 cells. Our work provides predictions for experimental
neuroscience studies. For example, our result suggests that a previous
neurophysiological study improperly discarded some of their recorded neurons;
we predict that their discarded neurons capture the shape contour of objects.

本文提出了一种基于改进的 RICA 模型的新方法，通过使用 sPCA 算法，模拟了人类视觉处理中不同层级的感受野，可以帮助理解人类视觉的功能和实现计算机视觉的研究。