Efficient Visual Coding: From Retina To V2
Authors: Honghao Shan, Garrison Cottrell
ICLR 2014

Abstract: 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.


Peer review:
[Review]:
Review Efficient visual coding: from retina to V2:

This paper tweaks the recursive ICA model (RICA). RICA made ICA stackable by combining PCA and ICA, and applying a component-wise nonlinearity to the outputs of each layer. This version replaces PCA by sparse PCA.

When trained on natural images, the updated pipeline produces standard images of local center-surround receptive fields, and oriented edge detectors, and also complex-cell-like units on the second layer. 

sPCA is not new, nor is RICA, so there isn't much of a technical contribution. The results look good and the overall system is simple and stackable. There isn't much new but other researchers might want to see what representation this system learns.

However, I would like to see: 

- a discussion of how the results after training on natural images differ from RICA: is this only that the lower layer now has local-center-surround receptive fields?
- many more references to work by other people that also used deep architectures to create V2-like cells; the 6 references given here are woefully insufficient and this work cannot be presented in a vacuum and cite only the authors' own RICA. e.g.:

Karklin and Lewicki, Learning higher-order structures in natural images.
Network: Computation in Neural Systems, 14:483–499, 2003

Kevin Jarrett, Koray Kavukcuoglu, Marc'Aurelio Ranzato and Yann LeCun: What is the Best Multi-Stage Architecture for Object Recognition?, Proc. International Conference on Computer Vision (ICCV'09), IEEE, 2009

H Lee, C Ekanadham, A Ng
Sparse deep belief net model for visual area V2
Advances in neural information processing systems 20, 873-880