Binocular disparity is the subtle difference between the left and right images. The left eye sees more of the scene than the right eye to the left of the centre and vice versa. Neighbouring layers responding to the left and right eye can inhibit one another when necessary. Optic nerves from the eye join at the optic chiasm and some of the fibres decussate. Optic nerves contain axons that emanate from retinal ganglion cells in the eye. Regardless if the fibres decussate, all the fibres pass through the lateral geniculate nucleus. From the lateral geniculate nucleus, fibres feed into the striate cortex. Importantly, the striate cortex preserves the neighbourhood relations between the retinal ganglion cells. In other words, the striate cortex has a retinotopic map. The map is stretch and magnified around the fovea, which is consistent with the quality of foveal vision. Also the quality reflects the number of cells dedicated to this part of the retinal patch.
Types of Retinal Ganglion Cells
First, the midget retinal ganglion cells are the most common, making up about 80%. These cells respond to static form and project to the parvocellular layer of the lateral geniculate nucleus, specifically bilayers 3 through 6. Second, the parasol retinal ganglion cells consist of 8-10% of all retinal ganglion cells. These cells contain on/off receptive fields and receive their light input from rods. As expected, they respond to increases and decreases in light conditions. In addition, they also respond to motion. Output from the parasol retinal ganglion cells project to the magnoceullar layer of the lateral geniculate nucleus, specifically to bilayers 1 and 2. Thirdly, the bistratified retinal ganglion cells make up less than 10% of all retinal ganglion cells. They respond to short (blue) wavelengths by increasing their frequency rate and to middle wavelengths (yellow) by decreasing their frequency rates. Output then projects to the konio sublayers 3 and 4. Lastly, the biplexiform retinal ganglion cells are equally rare making up less than 10%. The exact function of these cells in unknown, but it is known that they connect directly to rods and contain on-centre receptive field. It is believed that they provide information about ambient light.
Features of the Lateral Geniculate Nucleus
The nucleus consists of six major layers; each layer has a major layer plus a konio cell sub-layer. Each layer is responsible for carrying information from one eye. All of the layers of one lateral geniculate nucleus receive input from half the visual space. Even though layers 2, 3 and 5 correspond with the left eye, they only receive half of the information of the retina in that eye; the other half corresponds to the right visual field. As with the striate cortex, cells in each layer are organised retinotopically. In addition, each layer encodes a different aspect of the retinal image. Each lateral geniculate nucleus contains twelve copies of half the visual field (2/bilayer). However, it is important to note that only 10% of inputs come from the retina. 30% of input comes from outputs including the striate cortex and the midbrain. Thorpe (1996) proposed that the brain uses feed forward connections of retina to lateral geniculate nucleus to striate cortex to perform “quick and dirty” analysis. Feedback then makes connections to the retinal image. Thorpe’s hypothesis is supported by his demonstration that people can make quick visual interpretations from briefly flashed images.
The Striate Cortex (V1)
The striate cortex is responsible for early feature detection representations including colour. Stimulation of the striate cortex produces hallucinations of swirling colour (Frisby and Stone, 2010). In additional, all cells in the striate cortex have orientation-tuned columns except for layer 4B. The LGN and retinal ganglion cell are not orientation-tuned like the striate. Like the lateral geniculate nucleus, however, it is organised in layers: horizontal, vertical and retinotopy. The top layer of V1 contains pyramidal cells and their dendrites. On the other hand, the bottom layer of V1 contains pyramidal cells as they exit the cortical layer. Neurons in these layers are arranged into vertical columns with each column dedicated to one retinal patch and a specific characteristic. Retinal progress decreases the further you move from the edge of V1.
In 1978 a study was carried out by Hubel et al. to illustrate the existence of orientation-tuned cells in the striate cortex. Anesthetized macaque monkeys had their eyes exposed to a pattern of vertical stripes, continuously for 45 minutes. The stripes were of irregular width, filled the entire visual field, and moved about to activate the entire striate cortex. A chemical was then injected to be taken in by any active cells. Immediately afterwards, an autopsy was performed, which showed increased chemical uptake in the vertical columns.
Now these orientation-tuned columns are called hypercolumns. Each hypercolumn contains a mass of different types of cells that together process the same retinal patch. The patch of the retinal image that each hypercolumn deals with is called the hyperfield. Hyperfields must overlap to some degree, which allows for edge features to be detected (Frisby and Stone, 2010). As well as overlap, inter-hypercolumn communications links edge features together to create on unified image. This communication is possible due to the horizontal fibres that run along the vertical columns. The area dedicated to a particular area remains quite constant; however, processing decreases the further you move from the centre, which is why feature detection becomes cruder the further you move into the periphery. In addition to orientation, these cells can also be tuned to colour, scales and ocularity (Frisby and Stone, 2010). The ice-cube model (Hubel and Weisel, 1962) argues that each hypercolumn functions as an image-processing mechanism.
Convultion images are used to give us an idea about the activity profile of the striate cortex, representing the output of simple cells (Frisby and Stone, 2010). Each point on a convultion image represents the response of a single simple cell, which is centered over the corresponding retinal image. White represents a large, positive output. Grey represents no output, and black represents a large negative output. Based on this scale, outputs are coded in terms of pixel grey level. Inside a hypercolumn is a pattern of activity corresponding to one area (point) of the convultion image, an area representing the hyperfield (ibid).
Unlike simple cells, complex cells cannot be mapped into positive and negative regions. An optimal stimulus does not need fall on any particular region of the retinal field. However, a line or slit of a particular orientation is still the preferred stimulus. A theory of complex cells and receptive fields has been proposed (Frisby and Stone, 2010). This theory proposes that the receptive fields of complex cells can be predicted supposing they receive their input from a series of suitably placed simple cells. However, this theory cannot be true because some complex cells do not even receive input from the striate cortex.
Hypercomplex or “end topped” cells cannot be mapped into positive or negative regions either, but unlike simple and complex cells, they prefer moving stimuli. In addition, hypercomplex cells are selective to stimulus length. Of all stimuli, the best is either a bar of defined length or a corner.