A major limitation to our understanding of visual cortical function is the lack of computational theories capable of making useful, testable predictions for what the cortex should be doing. The purpose of this study is to investigate what may be learned about information processing in visual cortex from efficient coding principles. Methods will be developed for representing the structure in images based on probabilistic inference, and these will be related to known neurobiological substrates in a detailed manner in order to make predictions about visual cortical function. Understanding how the cortex processes visual information is an important step in developing therapies for patients who have lost aspects of visual function due to cortical damage, as well as in the development of visual prostheses capable of providing appropriate cortical stimulation from artificial vision devices. The aspects of visual cortical function that the study aims to shed light on are the properties of feature selectivity, form-invariance, and the role of feedback connections in shaping neural response properties and in mediating visual perception. These issues will be addressed as part of five specific aims. The first is to develop a functional model of the horizontal connections in area V1 based on the statistical structure of natural images. This model will be related to the structure of long-range horizontal fibers in order to make predictions about the role of this form of feedback within V1.
The second aim i s to develop a model neural system capable of learning the structure of objects independent of variations in position, size, or other geometric transformations. The model will be used to help understand how form-invariance is established in cortical neurons.
The third aim i s to formulate a model of occlusion in images, which will be used to shed light on how figure-ground segregation could be performed by cortical mechanisms.
The fourth aim i s to develop a functional model of top-down cortical feedback based on a hierarchical image model. The existence of such a system that utilizes top-down feedback to solve practical problems in vision will help to elucidate a possible role for two-way information processing in the cortical hierarchy. The fifth aim is to test these models through psychophysical experiments. The results of these studies will lead to advances in our understanding of information processing in visual cortex, and possibly shed light on the nature of cortical information processing in general.
| Johnson, Jeffrey S; Olshausen, Bruno A (2005) The earliest EEG signatures of object recognition in a cued-target task are postsensory. J Vis 5:299-312 |
| Johnson, Jeffrey S; Olshausen, Bruno A (2005) The recognition of partially visible natural objects in the presence and absence of their occluders. Vision Res 45:3262-76 |
| Barlow, Horace B; Olshausen, Bruno A (2004) Convergent evidence for the visual analysis of optic flow through anisotropic attenuation of high spatial frequencies. J Vis 4:415-26 |
| Murray, Scott O; Schrater, Paul; Kersten, Daniel (2004) Perceptual grouping and the interactions between visual cortical areas. Neural Netw 17:695-705 |
| Johnson, Jeffrey S; Olshausen, Bruno A (2003) Timecourse of neural signatures of object recognition. J Vis 3:499-512 |
| Murray, Scott O; Kersten, Daniel; Olshausen, Bruno A et al. (2002) Shape perception reduces activity in human primary visual cortex. Proc Natl Acad Sci U S A 99:15164-9 |
| Press, W A; Olshausen, B A; Van Essen, D C (2001) A graphical anatomical database of neural connectivity. Philos Trans R Soc Lond B Biol Sci 356:1147-57 |
| Simoncelli, E P; Olshausen, B A (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193-216 |
