The Neuronal Organization of the Retina

Horizontal Cells

As neural populations, horizontal cells are equally simple. The large majority of mammals have two types of horizontal cells. Both of them feed back onto the rod or cone photoreceptors. Some rodents have only one type, and there have occasionally been proposals of a third type in some animals. Despite some variation in morphological detail, though, horizontal cells appear to follow a fairly simple plan (Müller and Peichl, 1993; Peichl et al., 1998). Horizontal cells provide inhibitory feedback to rods and cones and possibly to the dendrites of bipolar cells, though this remains controversial (Herrmann et al., 2011). The leading interpretation of this function is that it provides a mechanism of local gain control to the retina. The horizontal cell, which has a moderately wide lateral spread and is coupled to its neighbors by gap junctions, measures the average level of illumination falling upon a region of the retinal surface. It then subtracts a proportionate value from the output of the photoreceptors. This serves to hold the signal input to the inner retinal circuitry within its operating range, an extremely useful function in a natural world where any scene may contain individual objects with brightness that varies across several orders of magnitude. The signal representing the brightest objects would otherwise dazzle the retina at those locations, just as a bright object in a dim room saturates a camera’s film or chip, making it impossible to photograph the bright object at the same time as the dimmer ones.

Because the horizontal cells are widely spreading cells, their feedback signal spatially overshoots the edges of a bright object. This means that objects neighboring a bright object have their signal reduced as well; in the extreme, the area just outside a white object on a black field is made to be blacker than black. This creates edge enhancement and is part of the famous “center-surround” organization described in classic visual physiology (Hartline, 1938; Kuffler, 1953). But the inner retina contains many more lateral pathways than the outer, and creates both simple and sophisticated contextual effects. Indeed, Peichl and González-Soriano (1994) pointed out that the ganglion cells of mice and rats have a quite ordinary center-surround organization, but these retinas lack one type of horizontal cell altogether. Perhaps the horizontal cells are best imagined as carrying out a step of signal conditioning, which globally adjusts the signal for reception by the inner retina, rather than being tasked primarily with the detection of edges.

The synapses by which horizontal cells provide their feedback signals appear to use both conventional and unconventional mechanisms; they remain a matter of active investigation (Hirano et al., 2005; Jackman et al., 2011; Klaassen et al., 2011). Taken as morphological populations, however, the horizontal cells are relatively simple. They can be stained for a variety of marker proteins in different animals. They, too, have been quantitatively mapped across the retinal surface in many species (Collin, 2008).

Bipolar Cells

Early physiological recordings suggested that there were four types of bipolar cells: ON, OFF, sustained, and transient (Kaneko, 1970; Werblin and Dowling, 1969). Modern anatomical work and subsequent physiological evidence indicate that the true number of bipolar cell types is about 12. This has been a gradual realization. Initial studies used synapse densities (Cohen and Sterling, 1990) to distinguish the types. As marker proteins of increasing specificity were discovered, the number of putative bipolar cell types gradually increased. Recent studies seem to have brought this to its conclusion.

A set of intersecting methods was used to classify the bipolar cells of the rabbit (MacNeil et al., 2004). The strategy was to seek a complete survey of bipolar cell types by using several methods with different sampling biases. For purposes of classification, the purely anatomical samples were complemented by a set of cells injected with Lucifer yellow after physiological recording, so that their responses to light could be used as part of the classification. The bipolar cells of the rabbit were divided into a rod bipolar cell and 12 types of cone bipolar cells. In near-perfect agreement, Wässle et al. (2009) classified the bipolar cells of the mouse using immunostaining for recently discovered type-specific markers and transgenic strains in which one or a few types of bipolar cells express a fluorescent marker. These were supplemented by microinjection, to reveal the cells’ finest processes and their contacts. They found one type of rod driven bipolar cell and 11 types that receive inputs primarily from cones (Figure 2). Because they are population stains, these methods allowed an estimate of the total number of bipolar cells of each type, which could then be added up for comparison with the total number of bipolar cells known by independent methods to exist in the mouse (Jeon et al., 1998). The identified individual cell types correctly added up to the known total number of bipolar cells. Thus, “…the catalog of 11 cone bipolar cells and one rod bipolar cell is complete, and all major bipolar cell types of the mouse retina appear to have been discovered” (Wässle et al., 2009).

The Synapses of Cones with Bipolar Cells Create Parallel Informational Channels

This concept is simple, but it is topologically fairly subtle (Figure 3). From partial evidence, it was suspected a decade ago that each cone makes output to each of the types of bipolar cells—a critical principle for the signal processing of the retina. Wässle et al. (2009) could confirm that this occurs for each of the 11 types of bipolar cells that they identified in the mouse. The exception is a specialized “blue cone bipolar,” which selectively contacts the short wavelength sensitive cones, as is necessary if the chromatic information is not to be degraded. Symmetrically, some bipolar cells avoid the terminals—they are numerically infrequent—of blue cones. And there is some crosstalk with the rods. But the central principle, which dominates the structural and functional organization of the retina, is that each bipolar cell contacts all of the cone terminals within the spread of its dendritic arbor. This is a geographically simple rule.

Functionally, however, this arrangement allows something more sophisticated. By tuning the characteristics of the cone-to-bipolar synapses, each type of bipolar cell can transmit a different parsing of the cone’s output. Bipolar cells express distinctive sets of receptors, ion channels, and intracellular signaling systems. This right away suggests that each of the cells has a unique physiology, and so far that has consistently turned out to be the case. As a consequence, it is believed that each of the ~12 anatomical types of bipolar cell that contacts a given cone transmits to the inner retina a different component extracted from the output of that cone.

What types of information are segregated into the dozen parallel channels? A simple case is the blue cone bipolar. In the inner retina, this type of bipolar cell contacts a ganglion cell that compares short and long wavelengths; the ganglion cell then becomes a blue-ON, green-OFF ganglion cell. In the ground squirrel (a favorite because it contains a large number of cones), the bipolar cells that contact both classes of cones have been shown to have the expected broad spectral sensitivity, and presumably transmit the simple brightness of a stimulus, independent of its color (Breuninger et al., 2011; Li and DeVries, 2006).

Among the non-chromatic bipolar cells, a classic example is the segregation of responses into ON and OFF channels, the ON channels having their axon terminals in the inner half of the inner plexiform layer (IPL) and the OFF bipolars having their terminals in the outer half (Famiglietti et al., 1977; Nelson et al., 1978). The difference between ON and OFF responses is due to the expression of two classes of glutamate receptor. OFF bipolar cells express AMPA and kainate type receptors, which are cation channels opened by glutamate; since photoreceptor cells hyperpolarize in response to light, these bipolar cells hyperpolarize in response to light as well, because less glutamate arrives from the cone synapse. ON bipolar cells express mGluR6, a metabotropic receptor, which, when glutamate binds to the receptor, leads to closing of the cation channel TRPM1. The receptor is thus sign inverting. When light causes less glutamate to be received from the photoreceptor terminal, cation channels open and the cell depolarizes (Morgans et al., 2009; Shen et al., 2009).

Similarly, the distinction between sustained and transient bipolar cells is caused by the expression of rapidly or slowly inactivating glutamate receptors (Awatramani and Slaughter, 2000; DeVries, 2000). This creates four classes of bipolar cells: ON-sustained, ON-transient, OFF-sustained, and OFF-transient. In detail, the different structural/molecular types of bipolar cells show a wide diversity of response waveforms in response to light; aside from the simple tonic versus phasic dimension, these responses display complex mixtures of the two (Wu et al., 2001). The functional meanings of these are only beginning to be understood (Freed, 2000). A case in point is the expression of differing sets of regulation of G protein signaling (RGS) proteins, which control the kinetics of the response to synaptic input in ON bipolar cells (Cao et al., 2012). Another is a type of bipolar cell that generates Na⁺ action potentials. Na⁺ currents have been known to occur from studies of many retinas, but their functions are unclear (Ichinose and Lukasiewicz, 2007; Ichinose et al., 2005; Ma et al., 2005; Zenisek et al., 2001). In the ground squirrel, the structurally defined bipolar cell termed cb5b has a large tetrodotoxin (TTX)-sensitive Na⁺ current. These cells signal the onset of a light step with a few all-or-nothing action potentials (Figure 4). In response to a continually graded noise stimulus (more closely representing a natural scene), they generate both graded and spiking responses, the spikes occurring with millisecond precision. The cells select for stimulus sequences in which transitions to light are preceded by a period of darkness. Their axon terminals costratify with the dendrites of a specific group of ganglion cells, and these ganglion cells encode light onset with a short latency burst of spikes. It thus appears that this bipolar cell trades the band-width inherent in graded signaling for spikes that can elicit a rapid and reliable response in transient-type ganglion cells (Saszik and DeVries, 2012).

Three Generalizations about Amacrine Cell Functions

First, amacrine cells create contextual effects for the responses of retinal ganglion cells. This includes the classic “center surround” antagonism, but also a variety of other, more subtle, effects (review, Gollisch and Meister, 2010). A nice example is object motion detection, a phenomenon in which a retinal ganglion cell responds to stimulus motion, but only to motion relative to the overall background of the scene. This provides a signal that distinguishes true motion of an object in the world from self-induced motions of the observer, especially eye movements, which cause everything to shift across the retina at the same time (Figure 6). Interestingly, this computation was observed for only a subset of retinal ganglion cells. The plethora of wide-field amacrine cells suggests that other context dependencies, as yet unimagined, remain to be discovered.

Second, many amacrine cells—perhaps a majority of the total number—perform some variety of vertical integration (the term is meant to contrast with lateral integration, as carried out by horizontal and wide-field amacrine cells). Only a small fraction of the 13 narrow field amacrine cell types found by MacNeil et al. (1999) were restricted to branching in narrow strata; the rest communicate among several, sometimes all, of the layers of the IPL, like the cell shown in Figure 5. This means that they carry ON information into the OFF strata, and vice versa. This is termed crossover (for the crossing between ON and OFF layers) inhibition (because amacrine cells release GABA or glycine). It is the subject of very active investigation, which reveals a variety of interesting controls on the flow of information through the retina. The details are beyond the scope of this review, but an example is the finding that some “excitatory” responses of ganglion cells to light are actually a release of amacrine mediated inhibition (Buldyrev et al., 2012; Demb and Singer, 2012; Farajian et al., 2011; Grimes et al., 2011; Molnar et al., 2009; Nobles et al., 2012; Sivyer et al., 2010; Werblin, 2010).

Third, most of the functions of amacrine cells are narrowly task-specific. An example is amacrine cell A17, a widely spreading neuron that places hundreds of electrotonically isolated synaptic boutons in contact with the output sites of the rod bipolar cell. At those points, the amacrine cell feeds back an inhibitory signal that improves the fidelity of information transmission by the rod bipolar cell (Grimes et al., 2010; Sandell et al., 1989). This is the A17 cell’s primary, perhaps sole, task: and the A17 amacrine is in any case irrelevant to events that happen under daylight conditions. Another highly specialized amacrine cell, recently discovered in the ground squirrel retina, creates a specific receptive field property in a single type of ganglion cell (Chen and Li, 2012; Sher and DeVries, 2012). A blue-ON ganglion cell is well-known: it is excited by the blue-ON bipolar cell that selectively contacts blue cones. But electrophysiological recordings have encountered a blue-OFF ganglion cell, inhibited when the stimulus lies at the short wavelength end of the spectrum. How can this happen if the only path through the retina is the blue-ON bipolar, carrying an excitatory signal? It turns out that a specific amacrine is driven directly by the blue-ON bipolar cell. The amacrine cell, like virtually all amacrine cells, is inhibitory to its postsynaptic partners. When excited by the blue-ON bipolar cell, this amacrine cell performs a sign inversion: it inhibits the ganglion cell upon which it synapses, thus creating a ganglion cell that is selective for blue stimuli and responds to a blue stimulus by slowing its firing—a blue-OFF ganglion cell.

A final task-specific case is the role of the starburst amacrine cell. In 1965, Horace Barlow and William Levick reported that certain ganglion cells of the rabbit retina respond selectively to the direction of stimulus motion, and, in a report classic for its intelligence and detail, described the key features of the cells (Barlow and Levick, 1965). The directional preference is the same for all small regions within the receptive field of the cell; a ganglion cell with a receptive field 500 μm in diameter can discriminate 40 μm movements anywhere within its receptive field (Figure 7). This “local subunit,” is a critical property because it distinguishes this discrimination from a trivial form of direction selectivity that can be predicted simply from the presence of adjacent ON and OFF regions. It is direction per se that the cell detects, not any simple spatial pattern of excitatory and inhibitory zones.

The search for a mechanism settled eventually on the starburst amacrine cell. Critically, the starburst cells have enormously overlapping dendritic arbors (Tauchi and Masland, 1984). The starburst cells do not tile the retina; they shingle the retina, like roofing shingles, and it was suggested that the reason for their apparent redundancy of coverage was to create the local subunit of the DS receptive field (Masland et al., 1984). In 1988, Vaney and Young proposed what turned out to be the correct mechanism of direction selectivity (Figure 7). They suggested that (1) individual sectors of the starburst dendritic arbor act as independent units, (2) dendritic sectors of the starburst cell pointing in a single direction selectively synapse upon any individual DS ganglion cell, and (3) these sectors are individually direction selective, creating a directional input to the ganglion (Vaney, 1991; Vaney and Young, 1988).

A direct test of this idea came from paired recordings between a DS cell and an overlapping starburst cell (Fried et al., 2002). As predicted, stimulation of a null-side starburst cell produced a GABAergic inhibition of the cell, while stimulation of starburst cells at other locations produced only a mild excitation (Lee and Zhou, 2006). At about the same time, two photon Ca²⁺ imaging showed that the sectors of a starburst cell are indeed functionally isolated units, and that they are directionally polarized in their responses, with greater Ca²⁺ influx resulting from stimulus movement outward (away from the soma) than inward (Euler et al., 2002). The coup de grace was provided by Briggman et al. (2011), who used high-throughput electron microscopic reconstruction (see below) to confirm that starburst cells pointing in the null direction selectively contact the DS ganglion cell. This work is discussed in a definitive recent review (Vaney et al., 2012).

Mosaics, Tiling, and How Retinal Cells Survey the Visual Scene

How does the multitude of retinal cells array itself across the retinal surface? The answer reveals an elegant feat of developmental engineering (review, Reese et al., 2011). Each of the retina’s >60 cell types is regularly spaced, so that the cells cover the retinal surface evenly. This assures that the cell types survey the visual scene efficiently (Cook, 1996; Wässle et al., 1981; Wässle and Riemann, 1978). But retinal cells of a particular type are evenly spaced only with respect to other cells of the same type. With respect to cells of other types—even those to which they are synaptically connected—their positions are random (Rockhill et al., 2000). Not only do the cell bodies space themselves, the dendritic arbors of most cell types arrange not to overlap very much, as though dendrites of neighboring cells of the same type repel each other. This efficient coverage is observed physiologically as well as morphologically (Devries and Baylor, 1997; Gauthier et al., 2009).

The phenomenon is called “tiling,” but the term—invoking bathroom tiles—conflates two different concepts: regular spacing of the cell bodies (mosaic spacing), and fitting together of the dendritic arbors at their edges. A measure of the latter is the coverage factor, given by the spatial density of the cells (cells/mm²) times the dendritic field area of each cell (mm²/cell). A coverage factor of 1.0 represents perfect tiling: no empty spaces between the arbors, and no overlap between the arbors. Bathroom tiles have both a regularly spaced mosaic and a coverage factor of one. All genuine cell types thus far discovered have regular mosaics. Many ganglion cells and the axon terminals of bipolar cells have coverage factors near 1.0. Other types of ganglion cells, especially in lower mammals, have coverage factors of three to five, and thus partial overlap in their arbors. And wide-field amacrine cells have enormous coverage factors, representing the specialized functions of these cells. The starburst amacrine cell of a rabbit has a coverage factor that ranges from 25 centrally to 70 peripherally, an overlap that serves their unique function for direction selectivity.

Because of their regular spacing, the arbors of each of the ~20 types of retinal ganglion cells cover the retina completely and evenly. This means that every point in the retinal surface is reported upon at least once—in the limiting case, exactly once—by each of the diverse types of retinal ganglion cell. This is represented pictorially in Figure 8, where the mosaics of four different types of ganglion cell are superimposed on an image. The first represents the X-type cell, responding in a linear way to the total brightness captured within its aperture. The second represents the Y cell, with a larger aperture and sensitivity to movement. The third represents a DS cell, responding to movement in a particular direction. The last represents the blue-ON (or blue-OFF) ganglion cell, transmitting the mean spectral luminance along the spectrum from blue to green. These tilings are independent, so that the mosaics are simultaneously superimposed upon each other. The same principle holds for the remaining functional types of ganglion cell, so that every point in the visual scene is simultaneously reported to the brain by ~20 independent filters, each transmitting a different aspect of the stimulus.

The Brain Must “Bind” More Representations Than Previously Thought

A large field cell (alpha cell) can tell the brain that something is moving, but cannot specify where, within a large area, the moving thing is located. How the brain incorporates this information into useful perception is part of the classic “binding problem,” important for both experimentalists and theorists. The problem is more than binding a signal about form and a signal about motion; there are several types of signal about form, there is the directionality of motion, etc. The local edge detector (not the X cell) is the most numerous type of retinal ganglion cell in the mouse and rabbit retinas (van Wyk et al., 2006; Zeck et al., 2005; Zhang et al., 2012). Why does the mouse retina use this instead of (or in addition to) an X cell? All of the retinal encodings must converge to a unified representation of the visual world. Where does this convergence occur? Do they converge in primary visual cortex, or could the diverse retinal encodings create multiple, as-yet-unrecognized, parallel streams in higher visual centers? If they converge in primary visual cortex, what is the consequence for receptive fields encountered there?

Ganglion Cells Have Context-Dependent Dynamic Properties

The classic descriptions of ganglion cell receptive fields were essentially static—the term “receptive field” has its roots as a spatial “field.” But a host of dynamic properties have now been discovered. These include a wide variety of contextual influences, such as the object motion segmentation, shown in Figure 6; a response to “looming” stimuli, saccadic suppression of ganglion cell responses, and most recently, new forms of direction selectivity and anticipatory responses to moving stimuli (Hosoya et al., 2005; Münch et al., 2009; Ölveczky et al., 2003; Roska and Werblin, 2003). In the latter case, it can be shown that retinal movement-sensitive neurons begin responding before they should, based on static mapping of their receptive field; their responses anticipate the incursion of a moving stimulus. This is an instructive example, because it is yet another case in which the retina’s responses are tuned to the probabilistic structure of the natural world. A moving stimulus is more likely than not to continue along a straight path; the retina gains an advantage in speed by predicting that this probable stimulus will continue (Schwartz et al., 2007). A related example is the retina’s numerical bias toward OFF cells, which mirrors a bias toward darkening events in the natural world (Ratliff et al., 2010). Perhaps this matching to the statistics of natural scenes will provide clues to the response tuning of the many as-yet-unclassified types of retinal ganglion cells.

Plasticity and Overlapping Functions Allow the Brain to See Well using Varied Retinal Inputs

It is a commonplace among clinicians that a very small number of surviving retinal ganglion cells allows substantial vision. A subtler point is made by the clinical condition of stationary night blindness, which results from an inactivating mutation in mGluR6, the glutamate receptor expressed by ON bipolar cells or its signaling partners. This eliminates roughly half of the light-evoked signals that the retina sends to the brain. To be sure, patients with this mutation (or monkeys in which ON responses are blocked by excess of an mGluR6 agonist) lose their night vision, because the rod bipolar cell is an ON bipolar and signals from rods then reach the inner retina only under limited circumstances. In ordinary daylight, however, they are remarkably little handicapped, manifesting a deficit that is only revealed by specialized testing. Whether this represents plasticity—a literal rewiring of central visual circuits—or just the wealth of information present in even a partial retinal output, remains to be learned (Dryja et al., 2005; Maddox et al., 2008; Schiller et al., 1986; van Genderen et al., 2009).

There is also evidence that the brain can correctly interpret new information transmitted down the same old wires. This comes from experiments in which gene transfer was used to create trichromatic vision in normally dichromatic animals—to cure their color blindness. The experiment is to speed up evolution—to artificially create new cone types and see how vision is changed. Would changing the color selectivity of the cones produce different visual capabilities in the animal, or would the animal simply be confused? This has been done in two different experiments. In the first, Jacobs and colleagues created a mouse strain that expresses in some of its cones a red opsin, sensitive to wavelengths longer than those of the normal green opsin (normal mice have the usual pattern of one short and one long wavelength opsin). These mice see further into the red than any mouse has ever seen before. More importantly, careful behavioral experiments show that they can use their new three-cone array to have true trichromatic color vision (Jacobs et al., 2007).

The second experiment had two differences: first, it was carried out in the monkey; second, the transgene for the new opsin was introduced into adult animals instead of being present throughout the animal’s life. Most New World monkeys are natural dichromats; they have the generic mammalian array of two cone types. The experiment was to virally introduce a third opsin, on top of the already existing green opsin, into the green cones. Each cone thus contained the blue opsin, the green opsin, or the green opsin plus a red opsin. Even though their spectral sensitivity is mixed, the transgenic cones have a spectral tuning distinct from that of the green cone; functionally, they constitute a third type of cone. Behavioral testing showed that these monkeys have trichromatic color vision. Since no special neural circuitry for dealing with the red-green axis was introduced, the result means that the brain had learned to use the new chromatic information without any neural circuits purpose-built for red-green color vision.

The exact circuits that mediate the restored red-green vision are still being worked out—both for the retina and for higher visual centers—and alternative, though somewhat forced, explanations exist. (For a thoughtful review, see Neitz and Neitz, 2011). No matter what circuits one assumes to be in play, however, these animals must necessarily make the discrimination by using inputs that are different from the ones with which the animal was born.

Quite aside from its implications for the evolution of color vision, the finding is encouraging for certain proposed treatments of human blindness. In retinitis pigmentosa and age-related macular degeneration, blindness often results from degeneration of the retina’s rods and cones. In patients who suffer from these conditions, many neurons of the inner retina survive. Thus, simple vision might be restored by an optogenetic strategy, in which a new light-sensitive protein is inserted, by gene therapy methods, into the surviving bipolar or ganglion cells. Proof of this principle has been accomplished in mice that were blind because of inherited photoreceptor degenerations analogous to those that occur in humans (Lagali et al., 2008; Lin et al., 2008). Several different ways of reaching the goal are being tried, but whichever optogenetic manipulation proves to be best, it will almost certainly send to the brain an encoding of the visual stimulus different from the native one (Busskamp et al., 2010; Caporale et al., 2011; Greenberg et al., 2011; Polosukhina et al., 2012). That the brain can use new chromatic signals suggests that it will also be able to use new kinds of spatial signals, encouraging the hope that some level of useful spatial vision might be restored in previously blind human patients.

We Do Not Understand Why the Brain Needs All of the Encodings that the Retina Transmits

The poster child is a type of retinal ganglion cell in the macaque monkey, named the “smooth cell,” for a distinguishing feature of its dendrites, and meticulously studied by Crook and her colleagues (Crook et al., 2008, 2009). It has a physiology indistinguishable—to standard testing—from the classic Y/parasol cell, nonlinearly summing its inputs so that it is particularly sensitive to stimuli that flash or move. And yet it is clearly a different cell: (1) the smooth cell is instantly distinguishable from parasol cells in dendritic morphology, (2) it has twice the dendritic field diameter of a parasol cell, and (3) it tiles the retina with a uniform mosaic independent of the mosaic of parasol cells. Thus, the smooth cells send to the brain a coding of the visual input similar to that of the parasol cells, but each smooth cell reports upon a region of visual space about four times as big as that sampled by a parasol cell. The smooth cells project to the lateral geniculate body, way station to the cortex. Why does the cortex need to view the same feature of the world through two different-sized apertures? Is there some other difference in the encoding transmitted by the smooth cell, something not revealed by testing with standard grating stimuli? And how do these separate representations combine to create visual perception? Perhaps the nonstandard visual signals are somehow incorporated into the canonical pattern of visual cortical responses (Hubel and Wiesel, 1965). The alternative is that a fundamentally new concept of higher visual processing will be necessary.

The Joys and the Frustrations of Genetically Labeled Cells

In the past few years, strategies for introducing fluorescent labels into subgroups of retinal neurons have appeared (Feng et al., 2000; Huang et al., 2003; Huberman et al., 2009; Kim et al., 2008; Siegert et al., 2009; Yonehara et al., 2008, 2009). The importance of this advance is hard to overstate. These mouse strains breach the barrier that neuronal diversity raises for electrophysiological studies: the rarity of encountering any particular cell type in repeated experiments. They allow the same ganglion or amacrine cell to be visually targeted for recording. Even if several cell types express the fluorescent marker, one can use the anatomy of the cells to separate them, so that a single type can repeatedly be patched or imaged. An example where the expression is almost “pure” is the Jam-B cell, a ganglion cell type with a curious, wedge-of-pie shape and its own version of direction selectivity (Kim et al., 2008). This cell had been reported in anatomical surveys, but no particular attention had been paid (indeed, one study—by the author of this review—mistakenly classified them as developmental accidents) until a mouse in which they were selectively labeled was available.

These mice will also be useful for validating the retina neurome, because they provide an additional criterion for what constitutes a cell type, but they have a limitation when it comes to accounting for the retinal cell populations. The creation of these mouse strains is still a highly inexact science. This compromises the endgame—the attempt to learn when the census of cell types is complete. Most of the strains that exist so far show mixed expression of the marker in several cell types, or expression in only parts of a true cell population. And there is no way to know anything about the cells that are NOT labeled—no way to know where the labeled cell stands in the whole population of ganglion cells and how many unlabeled cell types remain. How many cells remain for which no one has yet hit upon an effective promoter strategy? What is the true mosaic of genetically marked cells, when one cannot count on reporter expression to mark all of the cell type’s members? Sooner or later, when the molecular fundamentals of gene expression are under better experimental control, a precise algorithm for the creation of cell type-specific lines will be devised and these obstacles will be overcome. In the meantime, other methods will also be required.

High-Throughput Electron Microscopy

An approach that avoids the sampling problem is provided by high throughput electron microscopy, also known as connectomics (Anderson et al., 2009; Briggman and Denk, 2006; Denk et al., 2012; Denk and Horstmann, 2004; Kleinfeld et al., 2011; Lichtman and Sanes, 2008; Seung, 2009). The method, a descendent of early, hand-implemented, serial sectioning (Cohen and Sterling, 1991), requires still-developing computational methods, but even now it is extremely powerful. A small area of retina is serially sectioned and high-resolution images of every cell are reconstructed. In these images, the synapses between the cells can be identified and connections traced. Furthermore, the reconstruction can be made to include a cell of known physiological function, so that synaptic contributions to that particular cell’s response are identified (Briggman et al., 2011). The small size of the retina’s cells—the bane of electrophysiologists—suddenly becomes an asset, because the size of the necessary field and the number of sections are relatively small. This method proved itself in confirming the central postulate of direction selectivity, where special attention was paid to a particular set of amacrine-to-ganglion cell synapses. But it can also be used in less focused ways. For example, a patch of mouse retina 200 μm², which is well within the capability of reconstruction technology, contains ~1,500 bipolar cells (Jeon et al., 1998). On average, this would amount to 125 bipolar cells of each of 12 types, more than enough for an independent verification of the types defined using light microscopy and an analysis of their synaptic connectivity. The same could be done for narrow field amacrine and ganglion cells.

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