The<unk>  translates an optical image into neural impulses starting with the patterned excitation of the colour-sensitive pigments of its rods and cones, the<unk> 's photoreceptor cells. The excitation is processed by the neural system and various parts of the brain working in parallel to form a representation of the external environment in the brain. The cones respond to bright light and mediate high-resolution colour vision during daylight illumination (also called photopic vision). The rod responses are saturated at daylight levels and don't contribute to pattern vision. However, rods do respond to dim light and mediate lower-resolution, monochromatic vision under very low levels of illumination (called scotopic vision). The illumination in most office settings falls between these two levels and is called mesopic vision. At mesopic light levels, both the rods and cones are actively contributing pattern information. What contribution the rod information makes to pattern vision under these circumstances is unclear. The response of cones to various wavelengths of light is called their spectral sensitivity. In normal human vision, the spectral sensitivity of a cone falls into one of three subtypes, often called blue, green, and red, but more accurately known as short, medium, and long wavelength-sensitive cone subtypes. It is a lack of one or more of the cone subtypes that causes individuals to have deficiencies in colour vision or various kinds of colour blindness. These individuals are not blind to objects of a particular colour, but are unable to distinguish between colours that can be distinguished by people with normal vision. Humans have this trichromatic vision, while most other mammals lack cones with red sensitive pigment and therefore have poorer dichromatic colour vision. However, some animals have four spectral subtypes, e.g. the trout adds an ultraviolet subgroup to short, medium, and long subtypes that are similar to humans. Some fish are sensitive to the polarization of light as well. In the photoreceptors, exposure to light hyperpolarizes the membrane in a series of graded shifts. The outer cell segment contains a photopigment. Inside the cell the normal levels of cyclic guanosine monophosphate (cGMP) keep the Na+ channel open, and thus in the resting state the cell is depolarised. The photon causes the<unk> l bound to the receptor protein to isomerise to trans-retinal. This causes the receptor to activate multiple G-proteins. This in turn causes the Ga-subunit of the protein to activate a phosphodiesterase (PDE6), which degrades cGMP, resulting in the closing of Na+ cyclic nucleotide-gated ion channels (CNGs). Thus the cell is hyperpolarised. The amount of neurotransmitter released is reduced in bright light and increases as light levels fall. The actual photopigment is bleached away in bright light and only replaced as a chemical process, so in a transition from bright light to darkness the eye can take up to thirty minutes to reach full sensitivity. When thus excited by light, the photoceptor sends a proportional response synaptically to bipolar cells which in turn signal the<unk> l ganglion cells. The photoreceptors are also cross-linked by horizontal cells and amacrine cells, which modify the synaptic signal before it reaches the ganglion cells, the neural signals being intermixed and combined. Of the<unk> 's nerve cells, only the<unk> l ganglion cells and few amacrine cells create action potentials. In the<unk> l ganglion cells there are two types of response, depending on the receptive field of the cell. The receptive fields of<unk> l ganglion cells comprise a central, approximately circular area, where light has one effect on the firing of the cell, and an annular surround, where light has the opposite effect. In ON cells, an increment in light intensity in the centre of the receptive field causes the firing rate to increase. In OFF cells, it makes it decrease. In a linear model, this response profile is well described by a difference of Gaussians and is the basis for edge detection algorithms. Beyond this simple difference, ganglion cells are also differentiated by chromatic sensitivity and the type of spatial summation. Cells showing linear spatial summation are termed X cells (also called parvocellular, P, or midget ganglion cells), and those showing non-linear summation are Y cells (also called magnocellular, M, or parasol<unk> l ganglion cells), although the correspondence between X and Y cells (in the cat<unk>  and P and M cells (in the primate<unk>  is not as simple as it once seemed. In the transfer of visual signals to the brain, the visual pathway, the<unk>  is vertically divided in two, a temporal (nearer to the temple) half and a nasal (nearer to the nose) half. The axons from the nasal half cross the brain at the optic chiasma to join with axons from the temporal half of the other eye before passing into the lateral geniculate body. Although there are more than 130 million<unk> l receptors, there are only approximately 1.2 million fibres (axons) in the optic nerve. So, a large amount of pre-processing is performed within the<unk> . The fovea produces the most accurate information. Despite occupying about 0.01% of the visual field (less than 2° of visual angle), about 10% of axons in the optic nerve are devoted to the fovea. The resolution limit of the fovea has been determined to be around 10,000 points. The information capacity is estimated at 500,000 bits per second (for more information on bits, see information theory) without colour or around 600,000 bits per second including colour. Spatial encoding On-centres and off-centres of the<unk>  When the<unk>  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 cells than ganglion cells. 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<unk>  – 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 excitatory, and negative weighting as 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<unk> '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 channels embedded in the synapses 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<unk>  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<unk>  is the first step to separating out the various objects within the scene. As an example, the following 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. -1/8 -1/8 -1/8 -1/8 +1 -1/8 -1/8 -1/8 -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<unk> . 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.<unk> l ganglion cells are almost never perfectly balanced. * The table is square while the centre–surround structures in the<unk>  are circular. * Neurons operate on spike trains traveling down nerve cell axons. Computers operate on a single floating point number that is essentially constant from each input pixel. (The computer pixel is basically the equivalent of a biological photoreceptor.) * The<unk>  performs all these calculations in parallel while the computer operates on
Retina
Forebrain
Hindbrain
Barrel cortex
Brainstem
Auditory cortex
Electuary
Diencephalon