Editing Retina (section)

=== Spatial encoding ===
{{Further|Receptive field|label1=Receptive field, for figures and more information on centre–surround structures}}
[[File:Receptive field.png|thumb|upright=1.36|right|On-centres and off-centres of the retina]]

When the retina sends neural impulses representing an image to the brain, it spatially encodes (compresses) those impulses to fit the limited capacity of the optic nerve. Compression is necessary because there are 100 times more [[photoreceptor cell]]s than [[ganglion cell]]s. This is done by "[[decorrelation]]", which is carried out by the "centre–surround structures", which are implemented by the bipolar and ganglion cells.

There are two types of centre–surround structures in the retina – on-centres and off-centres. On-centres have a positively weighted centre and a negatively weighted surround. Off-centres are just the opposite. Positive weighting is more commonly known as [[Chemical synapse#Receptor binding|excitatory]], and negative weighting as [[Chemical synapse#Receptor binding|inhibitory]].

These centre–surround structures are not physical apparent, in the sense that one cannot see them by staining samples of tissue and examining the retina's anatomy. The centre–surround structures are logical (i.e., mathematically abstract) in the sense that they depend on the connection strengths between bipolar and ganglion cells. It is believed that the connection strength between cells is caused by the number and types of [[ion channel]]s embedded in the [[synapse]]s between the bipolar and ganglion cells.

The centre–surround structures are mathematically equivalent to the [[edge detection]] algorithms used by computer programmers to extract or enhance the edges in a digital photograph. Thus, the retina performs operations on the image-representing impulses to enhance the edges of objects within its visual field. For example, in a picture of a dog, a cat and a car, it is the edges of these objects that contain the most information. In order for higher functions in the brain (or in a computer for that matter) to extract and classify objects such as a dog and a cat, the retina is the first step to separating out the various objects within the scene.

As an example, the following [[matrix (mathematics)|matrix]] is at the heart of a computer [[algorithm]] that implements edge detection. This matrix is the computer equivalent to the centre–surround structure. In this example, each box (element) within this matrix would be connected to one photoreceptor. The photoreceptor in the centre is the current receptor being processed. The centre photoreceptor is multiplied by the +1 weight factor. The surrounding photoreceptors are the "nearest neighbors" to the centre and are multiplied by the −1/8 value. The sum of all nine of these elements is finally calculated. This summation is repeated for every photoreceptor in the image by shifting left to the end of a row and then down to the next line.

{| class="wikitable"
|-
| style="background:lightyellow;" | -1/8|| style="background:lightyellow;" |-1/8|| style="background:lightyellow;" |-1/8
|-
| style="background:lightyellow;" | -1/8|| style="background:lightblue;" |+1|| style="background:lightyellow;" |-1/8
|-
| style="background:lightyellow;" | -1/8|| style="background:lightyellow;" |-1/8|| style="background:lightyellow;" |-1/8
|}

The total sum of this matrix is zero, if all the inputs from the nine photoreceptors are of the same value. The zero result indicates the image was uniform (non-changing) within this small patch. Negative or positive sums mean the image was varying (changing) within this small patch of nine photoreceptors.

The above matrix is only an approximation to what really happens inside the retina. The differences are:
* The above example is called "balanced". The term balanced means that the sum of the negative weights is equal to the sum of the positive weights so that they cancel out perfectly. Retinal ganglion cells are almost never perfectly balanced.
* The table is square while the centre–surround structures in the retina are circular.
* Neurons operate on [[spike train]]s traveling down nerve cell [[axons]]. Computers operate on a single [[floating-point arithmetic|floating-point number]] that is essentially constant from each input [[pixel]]. (The computer pixel is basically the equivalent of a biological photoreceptor.)
* The retina performs all these calculations in parallel while the computer operates on each pixel one at a time. The retina performs no repeated summations and shifting as would a computer.
* Finally, the [[horizontal cell|horizontal]] and [[amacrine cell]]s play a significant role in this process, but that is not represented here.

Here is an example of an input image and how edge detection would modify it.

[[File:Edge-detection-2.jpg|frameless|upright=2.5|alt=input image]]

Once the image is spatially encoded by the centre–surround structures, the signal is sent out along the optic nerve (via the axons of the ganglion cells) through the [[optic chiasm]] to the LGN ([[lateral geniculate nucleus]]). The exact function of the LGN is unknown at this time. The output of the LGN is then sent to the back of the brain. Specifically, the output of the LGN "radiates" out to the V1 [[primary visual cortex]].

Simplified signal flow: Photoreceptors → Bipolar → Ganglion → Chiasm → LGN → V1 cortex

[[File:ERP - optic cabling.jpg|frameless|upright=2.273|alt=ERP optic cabling]]