Review The Neuronal Organization of the Retina Neuron

Review The Neuronal Organization of the Retina Neuron
Neuron
Review
The Neuronal Organization of the Retina
Richard H. Masland1,*
1Departments of Opthamology and Neurobiology, Harvard Medical School, 243 Charles Street, Boston, MA 02114, USA
*Correspondence: [email protected]
http://dx.doi.org/10.1016/j.neuron.2012.10.002
The mammalian retina consists of neurons of >60 distinct types, each playing a specific role in processing
visual images. They are arranged in three main stages. The first decomposes the outputs of the rod and
cone photoreceptors into 12 parallel information streams. The second connects these streams to specific
types of retinal ganglion cells. The third combines bipolar and amacrine cell activity to create the diverse encodings of the visual world—roughly 20 of them—that the retina transmits to the brain. New transformations
of the visual input continue to be found: at least half of the encodings sent to the brain (ganglion cell response
selectivities) remain to be discovered. This diversity of the retina’s outputs has yet to be incorporated into our
understanding of higher visual function.
Charles Darwin famously wrote that the eye caused him to
doubt that random selection could create the intricacies of
nature. Fortunately, Darwin did not know the structure of the
retina: if he had, his slowly gestating treatise on evolution might
never have been published at all. Among other wonders, the
neurons of the retina are tiny (Figure 1). The 100 million rod
photoreceptors appear to be the second most numerous
neurons of the human body, after only the cerebellar granule
cells. The retina’s projection neuron, the retinal ganglion cell,
has less than 1% the soma-dendritic volume of a cortical
or hippocampal pyramidal cell. Although the retina forms a
sheet of tissue only 200 mm thick, its neural networks carry
out feats of image processing that were unimagined even
a few years ago (Gollisch and Meister, 2010). They require
a rethinking not only of the retina’s function, but of the brain
mechanisms that shape these signals into behaviorally useful
visual perception.
The retinal neurome—the census of its component cells—
continues to be refined. An initial estimate of 55 cell types in
the retina (Masland, 2001) appears to have been something of
an underestimate. Our understanding of the fundamental plan
of the retina remains the same, but new image processing mechanisms are coming into view. My aims here are to see how close
we have come to a complete census, to review the principles
by which the diverse cell types are organized, to illustrate
some of the ways in which they create the retina’s abilities,
and to forecast the path by which we may progress. I will begin
by outlining three large rules that govern relations among the
retina’s neurons.
Principle #1: The Signal Generated by Any Individual
Cone Is Decomposed into 12 Different Components,
Each of Which Is Transmitted Separately to the Inner
Retina by a Structurally and Molecularly Distinct
Type of Bipolar Cell
The retina’s processing of information begins with the sampling
of the mosaic of rod and cone photoreceptors by the bipolar and
horizontal cells. The photoreceptors form a single sheet of regularly spaced cells. Rod photoreceptors, specialized for vision in
dim light, outnumber cone photoreceptors by about 20-fold in
266 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
all but a few mammalian retinas. All rods contain the same
light-sensitive pigment, rhodopsin. With one known exception
(so far), each cone contains one—and only one—of several
cone opsins, each with a different spectral absorption; as will
be discussed later, these are the basis of color vision. Both
rods and cones respond to light by hyperpolarizing. Rods and
the chromatic classes of cones can be easily identified in intact
retinas by morphology and by their expression of the different
opsins.
This review will pass lightly over the rod system, which molecular dating shows to have been a late evolutionary addition to
the retina’s tool kit. This is not to say that rods are unimportant,
nor that they are uninteresting. Yet the retinal circuitry truly
dedicated to rod function includes only four cell types: the rod
itself, a bipolar cell that receives input only from rods (‘‘rod
bipolar cell’’), an amacrine cell that modulates the bipolar cell’s
output, and an amacrine cell that feeds the output of the rod
system into the circuitry that processes information derived
from cones. A second pathway from rods to ganglion cells exists
in some animals (it involves gap junctions with cones), but in
either case the strategy is the same: the late-evolving rods inject
their signals into circuitry that had already developed to service
the cones (Famiglietti and Kolb, 1975; Nelson, 1982; Nelson
and Kolb, 1985; Sandell et al., 1989; Strettoi et al., 1990, 1994;
Strettoi et al., 1992).
The types of cones are structurally and, as far as is known,
functionally similar. (This review pertains primarily to mammalian
retinas.) Their functional types are defined by the opsin that
each type expresses. A generic mammal expresses one short
wavelength-sensitive cone and one long wavelength. Comparison of the two outputs forms the basis of most color vision.
The numbers of rods and cones are known with great precision.
They have been counted and their topography mapped for
dozens of mammalian and nonmammalian species. These
have been collected at http://www.retinalmaps.com.au (Collin,
2008). For humans and the common laboratory animals, the
accounting of photoreceptor cells is complete.
Horizontal Cells
As neural populations, horizontal cells are equally simple. The
large majority of mammals have two types of horizontal cells.
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Figure 1. By the Standards of Other CNS Regions, Retinal Neurons Are Miniscule
(A) The layers of the mouse retina. A single bipolar cell is shown in white (adapted with permission from Wässle et al. (2009)). (B) The bipolar cell shown in A is
reproduced at its correct scale on an image showing a cortical pyramidal cell. Cortical cell republished from (Gilbert, 1992).
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
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Figure 2. The Types of Bipolar Cells Observed in the Mouse Retina
Note the different stratification within the inner plexiform layer and the
molecular diversity of the cells. Reproduced with permission from Breuninger
et al. (2011).
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 organiza268 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
tion 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 coneto-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+
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Figure 3. Divergence of the Cone Output
into Separate Bipolar-Cell-Mediated
Channels
(A) A single bipolar cell contacts all of the cones
within its reach.
(B) The dendritic arbors of the bipolar cells can
overlap, and this means that each cone can be
contacted by more than one bipolar cell. In this
panel are shown two bipolar cells, each contacting
all of the cones within their reach. Where the two
bipolar cells partially overlap, they share a set of
cones. For illustration, consider that these two
cones transmit the same type of information away
from the cones (both are shown in red).
(C) The same two bipolar cells, contacting the
same sets of cones, but this time transmitting
different types of information away from the cones.
(D) In fact, the dendritic arbors of 12 types of
bipolar cells overlap at any point on the retina. This
means that the output of any particular cone is
sampled by 12 different bipolar cells. In principle,
each of these types of bipolar cell carries
a different reporting of the output of that cone.
Four of the bipolar cells that contact one of the
cone terminals (arrow) are shown here. Each of
these can carry a different signal about the cone’s
activity, as shown by the different colors in which
they are drawn.
(E) Left, structural diversity in three types of bipolar
cell from the ground squirrel retina. The overlap of
their dendrites shows that these three bipolar cells
contact a nearly identical, overlapping, set of
cones. Bipolar cells with axons that terminate in
the upper half of the IPL are OFF type, whereas
those that terminate in the bottom half are ON
type. To the right are shown the responses of three
morphological types of bipolar cells, two OFF and
one ON, during light flashes of different durations.
OFF bipolar cells hyperpolarize in the light and
produce a transient depolarization at light OFF;
ON bipolar cells display the opposite behavior. The
shapes of the cb2 and cb3 cell light responses
differ in subtle but characteristic ways. Previously
unpublished bipolar cell images and responses
are courtesy of Drs. Steven DeVries and Adam
Light (see DeVries, 2000).
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 bandwidth inherent in graded signaling for
spikes that can elicit a rapid and reliable response in transient-type ganglion cells (Saszik and DeVries, 2012).
Principle #2: The Outputs of These Bipolar Cell
Channels Are Sampled by Different Sets of Retinal
Ganglion Cells
The central structural characteristic that defines the 12 types of
bipolar cells is the level of the inner plexiform layer at which their
axons terminate. In other words, the bipolar cells receive input
from all of the cones within their reach, as just described, but
they terminate on very restricted sets of postsynaptic partners.
Distinction of functional types on this basis is confirmed by
molecular differences that correlate with types that have been
defined in this way. The specificity is again confirmed by the
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Figure 4. Bipolar Cells with Different Temporal Properties Give Rise to Ganglion Cells with Different Properties
For purposes of illustration, the spiking response of the ganglion cells is shown as though it were driven primarily by the bipolar cell, an approximation that ignores
the contribution of amacrine cells. It is important to note that amacrine cells exert substantial control over the responses to light of the bipolar cells themselves.
Amacrine cells have feedback synapses upon the axon terminals of the bipolar cells. The bipolar cells are small and electrotonically compact; as a consequence,
the response recorded at the soma of a bipolar cell includes the effects of feedback by amacrine cells to that bipolar cell (see text). The stimulus to the bipolar cells
was direct injection of current into a connected cone. The responses of bipolar cells are adapted from Saszik and DeVries (2012). Responses of the ganglion cells
and bipolar cells are schematic; they do not derive from paired recordings.
fact that different sets of ganglion cells (as well as amacrine cells)
costratify with them. These, too, represent distinct types: they
have different central projections, different physiologies, and
different molecular signatures. Although there is amacrine cell
crosstalk between the layers (see below) the bulk of the inner
retina’s connectivity occurs within the layers. The stalks of
bipolar cell axons, and the proximal dendrites of ganglion cells,
often pass through several laminae to reach their final level
of stratification, but few synapses are made with these connecting processes en passant: the main work of synaptic connectivity is done within the layers. Indeed, the lamination of the
inner plexiform layer is a fundamental guide to the retina’s wiring
diagram.
All bipolar cells and all ganglion cells are stratified—some in
narrow layers, some in broader ones, some in multiple ones,
but always stratified. One may imagine the array of bipolar cell
axon terminals as transmitting a cafeteria of stimulus properties,
among which the ganglion cell chooses depending on the type
of information that particular ganglion cell will finally transmit to
central visual structures. This connectivity builds the initial foundation of the response selectivity that distinguishes functional
types of ganglion cell: if the different retinal ganglion cells get
selective inputs from differently responding bipolar cells, they
are right away imbued with differing types of response to light
themselves. Note that these connections are not limited to the
one-to-one case—ganglion cells that stratify in several layers
can take some of their properties from one type of bipolar cell,
and other properties from a different one.
A slightly tricky conceptual issue should be clarified here.
There are two main influences upon the responses to light of
bipolar cells. As just described, the first is their synaptic drive
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from the rod or cone photoreceptors, as expressed through
the bipolar cells’ differing glutamate receptors and modified by
their signaling proteins and ion channels. These features are
intrinsic to the bipolar cells, controlled by the set of proteins
that each type of bipolar cell expresses. But the bipolar cells
are also influenced by inputs from amacrine cells (Figure 5),
and those effects are included in the bipolar cell’s ‘‘response
to light’’ as well. Bipolar cells are short, fat neurons (Figure 1)
and are electrotonically compact. Thus, a recording from the
soma of the bipolar cell does not simply monitor a signal transmitted from dendrite to soma to axon of the bipolar cell, like
watching a railway train pass a vantage point alongside its
tracks. Instead, a soma recording monitors the effects of all of
the bipolar cell’s inputs, including the signals that impinge on
its axon terminals from amacrine cells (Bieda and Copenhagen,
2000; DeVries and Schwartz, 1999; Euler and Masland, 2000;
Matsui et al., 1998; Saszik and DeVries, 2012). Thus, the output
of the bipolar cell onto the ganglion cell includes both the intrinsic
response properties of the bipolar cell and the actions of amacrine cells upon the bipolar cell. The bipolar cell is as much an
integrating center as it is a conduit from outer retina to inner.
Principle #3: The Partially Selective Responses
Mediated by Bipolar Cells Are Refined by Amacrine
Cells—A Few per Ganglion Cell Type—To Create Arrays
of Precisely Specific Ganglion Cell Subtypes
The second controller of the ganglion cell response is direct input
from amacrine cells. Amacrine cells occupy a central but inaccessible place in the retinal circuitry. Most are axonless neurons
and their lack of a clear polarity makes it hard to recognize the
sites of their inputs and outputs. Because of their multiple
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Figure 5. The Structure and Generalized Connectivity of Narrow
Field Amacrine Cells
(A) Type 7 glycinergic amacrine cell of the mouse retina. Note that this cell
communicates ‘‘vertically,’’ interconnecting the ON and the OFF layers of the
IPL. Cell image is adapted from Menger et al. (1998).
(B) Block diagram of amacrine cell pathways. Amacrine cells receive input from
bipolar cells and other amacrine cells. They make outputs back upon bipolar
cells, to ganglion cells, or to other amacrine cells. Thus amacrine cells
participate in feedback inhibition, feed-forward inhibition, and lateral inhibition.
A single amacrine cell can have all of these arrangements or a subset of them.
connectivity, they are hard to conceptualize: they feed back to
the bipolar cells that drive them, they synapse upon retinal
ganglion cells, and they synapse on each other (Figure 5; Dowling and Boycott, 1966; Eggers and Lukasiewicz, 2011; Jusuf
et al., 2005; Lin et al., 2000). Their great structural diversity
makes them a daunting target for experimentation. In the
absence of some feature—natural or man-made—that allows
a single type to be systematically targeted, obtaining an
adequate experimental sample is virtually impossible. But progress is being made, especially in cases where an amacrine cell
type is structurally distinctive or can be genetically marked.
An early survey of amacrine cell types counted 29 types of
amacrine cell in the rabbit retina (MacNeil et al., 1999; MacNeil
and Masland, 1998). How well has this estimate stood up, and
what have we subsequently learned about the functions of
amacrine cells? The answer to the first question is that there
has been no subsequent survey of this type, but there have
been no big surprises and nothing to suggest that the populations of amacrine cells in other species are less complex. Those
types of amacrine cells for which we have specific stains are
generally the same in other species. But there were two weaknesses to the original survey. First, some of the cells were classified on the basis of very few examples. So far, better methods
have confirmed the original descriptions (Wright and Vaney,
2000), but it is to be expected that they will need, at the very
least, a fine-tuning. Second, there was uncertainty about the
number of wide-field amacrine cell types, which can cover the
retina with a very small, absolute number of cells, and thus are
rarely encountered. Recent studies show that there are more
wide-field cells than originally described. If the traditional definition of a retinal cell type is followed, there would be at least
16 types of wide-field amacrine cell (Lin and Masland, 2006).
However, the difference between them is primarily that they
stratify at different levels. By far the most striking feature of
these cells is their huge spread (Figure 6), and it is economical
(though somewhat inconsistent) to classify them as a single
cell type that performs the same function for different sets of
partners. Using this definition, the total number of known amacrine cell types would remain around 30.
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
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Figure 6. Wide-Field Amacrine Cells Can
Span Most of the Surface of the Retina
(A) Whole-mount view of a wide-field amacrine
cell termed WA5-1 in the survey of Lin and
Masland (2006). This cell’s axonal arbor (green)
would affect visual stimuli falling in approximately half of the animal’s field of view. But the
cell receives input from only a limited region of
their dendritic fields (red), and presumably
the population of cells of this type seamlessly
affect images throughout the field, without the
gaps that appear when a single cell or only
a few of them are taken in isolation, as shown
in (B). It does not take a large number of these
cells to achieve the nearly complete axonal
(green) coverage of the retina shown in (C).
If we assume that the dendritic fields (ellipses)
nearly tile the retina, the network of axonal
processes is dense enough to affect the visual
input with an adequate spatial resolution.
In fact, the illustration shown here does not
achieve tiling of the dendritic fields. If we
assume a dendritic coverage of at least unity—
higher than is shown here—the axonal coverage
would blanket the retina at a very high density
indeed. This is the arrangement to be predicted
from other known types of retinal cells; whether
or not it pertains to this cell will await a population stain.
(D and E) These cells appear to mediate a variety
of contextual effects, in which visual events
surrounding a particular stimulus condition the
response of a ganglion cell to that stimulus.
An example is ‘‘object motion detection,’’ in
which objects that move relative to the general
visual field are preferentially reported to
the brain (Ölveczky et al., 2003). The effect of
this computation is artificially simulated in the
lower panels. A native image is shown in (D). The image transmitted to the brain after object motion enhancement is shown in (E): the retinal ganglion cells
respond most strongly to objects that are moving relative to the stationary surroundings. (D) and (E) reprinted from (Masland, 2003).
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 blueOFF 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 mm in diameter can
discriminate 40 mm 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
272 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
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
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Figure 7. The Cardinal Features of the
ON-OFF Direction-Selective Cell, and the
Mechanism by Which Direction Selectivity
Is Created
(A) The cell can discriminate the direction of motion
of small stimuli falling within its receptive field
(large circle), and it does not matter where within
the field the small stimulus falls—there is a local
subunit that is direction selective.
(B and C) The fundamental mechanism of direction
selectivity. (B) Shows the dendritic arbor of a starburst amacrine cell. A sector of the arbor (outlined
in red) is (1) an independent functional unit, electrically separate from the rest of the cell, and (2)
directionally polarized, such that it releases GABA
when the stimulus moves in one direction—left to
right in this example—and not in others. (C) Starburst sectors pointing in a single direction (red)
selectively synapse upon dendrites of an ON-OFF
DS ganglion cell (outlined by the black circle). In this
example, they would provide inhibition when the
stimulus moves from left to right. This cell would
thus have a preferred direction for movement
right-to-left and a null direction for movement
left-to-right. The sectors are smaller than the
dendritic field, thus accounting for the ganglion
cell’s ability to discriminate small movements
within the field. Other sectors of the starburst cell,
pointing in other directions, would contact other
direction selective ganglion cells; those cells would
prefer different directions of stimulus movement.
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).
Very Diverse Encodings of the Visual Scene
Are Sent to the Brain
Because inputs from bipolar and amacrine cells combine, the
number of functional types of ganglion cell exceeds the number
of types of bipolar cell (Taylor and Smith, 2011). Their classification has been a difficult problem—most or all of the ganglion cell
types have almost certainly been stained in one study or another,
but it has not yet been possible to achieve a definitive classification in any mammalian species. How many types of ganglion
cells exist? The number of putative ganglion cell types estimated
in a series of five recent studies in the mouse was 11, 12, 14, 19,
and 22 (review, Masland, 2012). New cell types have emerged
since those studies were conducted. The apparent number of
ganglion cell types depends a lot on how they are counted:
should ON and OFF variants of the same response pattern be
considered as one cell type or two? Do the four cardinal direction
preferences of DS cells represent four cell types or one? No
matter how one counts, the number of types is surely not less
than a dozen in any mammal yet studied, and many workers
feel that the minimal number of structurally distinct types in the
mouse, rabbit, cat, or monkey is in the neighborhood of 20.
What can be the uses of 20 types of ganglion cells? There is
more extensive information for the rabbit retina than any other.
The ganglion cell types for which a morphological/physiological
identification is secure are as follows: a local edge detector,
much like the ‘‘bug detector’’ described long ago in the frog by
Maturana et al. (1960); ON-tonic and OFF-tonic cells; blue-ON
and blue-OFF ganglion cells; an ON direction selective cell,
which projects to the accessory optic system and subserves
optokinetic nystagmus; an ON-OFF directionally selective cell,
function unknown; two large, ON-transient or OFF-transient
cells; a recently identified ‘‘transient ON-OFF ganglion cell,’’
which responds much like an ON-OFF DS cell but is not directionally selective and has a different stratification; a uniformity
detector, which responds to changes in the visual input by
decreasing its firing rate; cells selective to each of two preferred
orientations; and the sparse intrinsically photosensitive (melanopsin) cells, whose long-lasting responses to light synchronize
the circadian oscillator, drive pupillary responses, and carry out
other functions still being explored. In the mouse, a curiously
shaped cell with a weak form of direction selectivity has been
discovered, as has an apparent homolog of the local edge
detector (Amthor et al., 1989; Ecker et al., 2010; Kim et al.,
2008; Levick, 1967; Rockhill et al., 2002; Roska and Werblin,
2001; Schmidt et al., 2011; Sivyer et al., 2010, 2011; Taylor
and Smith, 2011; van Wyk et al., 2006, 2009; Vaney et al.,
2012; Venkataramani and Taylor, 2010; Zhang et al., 2012).
This may seem like a long list. Note, however, that there are
nine modality-specific channels for touch, five for taste, and
>300 for smell. Truly remarkable would have been for vision,
said to occupy 50% of the cortex in primates (Van Essen,
2004), to have only the two types of retinal ganglion cell
stressed in the standard canon. If we assume 20 morphologically distinguishable cell types, at least half of the structurally
identified ganglion cells of the rabbit still have functions that
have not yet been characterized. An even smaller fraction of
Neuron 76, October 18, 2012 ª2012 Elsevier Inc. 273
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the morphological cell types have been characterized in the rat,
mouse, cat, or monkey.
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
274 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
to the brain by 20 independent filters, each transmitting
a different aspect of the stimulus.
Implications for Central Visual Processing
The signals sent by the retinal ganglion cells to the brain are the
fundamental stuff of vision. Surprisingly, textbook accounts of
higher visual function take little notice of their diversity. Indeed,
the textbook view of spatial integration in the visual cortex is
built upon a retina that conveys only two types of signal—the
X and Y cells, M and P cells in the primate—to the brain. Trivial
explanations, such as the idea that the more complex retinal
cells project only to subcortical centers, are no longer tenable
(Dacey, 2004; Gollisch and Meister, 2010; Masland and Martin,
2007). Some emerging points are as follows:
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
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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 lightevoked 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
Figure 8. How the Retina Surveys the World
Each type of retinal ganglion cell tiles the retinal surface, and thus covers every
visual image completely. The tiles represented by different ganglion cell types are
independent of each other. The different types of retinal ganglion cell sample the
world through apertures of a different size (determined by the size of their dendritic field). Not only are their apertures different, the sensitivity to features of the
stimulus within that aperture is different. This series of panels represents a visual
image as sampled by four different types of ganglion cell of a generic subprimate mammalian retina, a subset from a total of about 20 ganglion cell types.
(A) The traditional X/midget/brisk-sustained cell. This cell has a small receptive
field center with an antagonistic surround, usually modeled as a difference of
positive and negative Gaussians. This cell linearly sums inputs falling within
its aperture. It therefore can effectively represent gradations of intensity.
(B) The classic alpha/Y/brisk-sustained cell, which nonlinearly sums its inputs
and is most sensitive to stimuli that change or move. Note that the sampling
aperture (receptive field size) of this cell is large. It can report that something is
moving, but cannot tell the brain where, within coarse bounds, the moving
object is located.
(C) Direction selective cells also have relatively large receptive fields. They
transmit the information that something is moving within the receptive field,
but, again, an individual cell cannot accurately tell the brain where inside the
receptive field the stimulus lies.
(D) Color coded cells. These report where on the axis from blue to green the
spectrum of light within the cell’s receptive field is located. In a generic
mammalian retina, which has only one short wavelength and one long wavelength cone, this information is transmitted by the blue-ON and blue-OFF
ganglion cells.
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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 purposebuilt 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 agerelated 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
276 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
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 Road to Completion
The broad view of the retina’s organization is now complete, but
it remains studded with approximations— ‘‘around thirty’’ types
of amacrine cell, ‘‘twelve to twenty’’ types of ganglion cell—and
little has been said about synaptic connectivity. How do we get
to the next level of precision? It is important here to recognize
that the aim is a possibly utopian one: we seek an exact enumeration of the retina’s component cell types. This is different from
the traditional view, which is that the brain is so hopelessly
complex (and plastic into the bargain) that the best hope is
only a description of selected neural subcircuits, containing
just a few types of neurons. Instead, the goal here is to be able
to say: ‘‘These are the cells of the retina, and the list includes
all the cell types that exist.’’ For rods, cones, horizontal, and
bipolar cells, our present census is pretty definitive: we can identify the cell types and we can describe them quantitatively. But
amacrine cells have been enumerated only in the rabbit retina,
and retinal ganglion cells remain a struggle. All workers agree
on their broad diversity, and different imaging methods repeatedly show the same cells; but a consensus on a classification
of the ganglion cell types has not emerged. How do we get to
a definitive description?
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.
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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 mm2, 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.
ACKNOWLEDGMENTS
I thank Dr. Steven DeVries for the electrophysiological traces shown in Figure 3
and for reading the section on bipolar cells. Members of the Jakobs/Masland
lab made helpful comments. The figures were made by Michael Becker. Susan
Cardoza copyedited and helped with the references. The author is supported
by NIH grant EY13399 and the Harvard Neurodiscovery Center.
REFERENCES
Amthor, F.R., Takahashi, E.S., and Oyster, C.W. (1989). Morphologies of
rabbit retinal ganglion cells with complex receptive fields. J. Comp. Neurol.
280, 97–121.
Anderson, J.R., Jones, B.W., Yang, J.H., Shaw, M.V., Watt, C.B., Koshevoy,
P., Spaltenstein, J., Jurrus, E., U v, K., Whitaker, R.T., et al. (2009). A computational framework for ultrastructural mapping of neural circuitry. PLoS Biol. 7,
e1000074.
Awatramani, G.B., and Slaughter, M.M. (2000). Origin of transient and sustained responses in ganglion cells of the retina. J. Neurosci. 20, 7087–7095.
Barlow, H.B., and Levick, W.R. (1965). The mechanism of directionally selective units in rabbit’s retina. J. Physiol. 178, 477–504.
Bieda, M.C., and Copenhagen, D.R. (2000). Inhibition is not required for
the production of transient spiking responses from retinal ganglion cells. Vis.
Neurosci. 17, 243–254.
Breuninger, T., Puller, C., Haverkamp, S., and Euler, T. (2011). Chromatic
bipolar cell pathways in the mouse retina. J. Neurosci. 31, 6504–6517.
Briggman, K.L., and Denk, W. (2006). Towards neural circuit reconstruction
with volume electron microscopy techniques. Curr. Opin. Neurobiol. 16,
562–570.
Briggman, K.L., Helmstaedter, M., and Denk, W. (2011). Wiring specificity in
the direction-selectivity circuit of the retina. Nature 471, 183–188.
Buldyrev, I., Puthussery, T., and Taylor, W.R. (2012). Synaptic pathways that
shape the excitatory drive in an OFF retinal ganglion cell. J. Neurophysiol.
107, 1795–1807.
Busskamp, V., Duebel, J., Balya, D., Fradot, M., Viney, T.J., Siegert, S.,
Groner, A.C., Cabuy, E., Forster, V., Seeliger, M., et al. (2010). Genetic reactivation of cone photoreceptors restores visual responses in retinitis pigmentosa. Science 329, 413–417.
Cao, Y., Pahlberg, J., Sarria, I., Kamasawa, N., Sampath, A.P., and Martemyanov, K.A. (2012). Regulators of G protein signaling RGS7 and RGS11 determine the onset of the light response in ON bipolar neurons. Proc. Natl. Acad.
Sci. USA 109, 7905–7910.
Caporale, N., Kolstad, K.D., Lee, T., Tochitsky, I., Dalkara, D., Trauner, D.,
Kramer, R., Dan, Y., Isacoff, E.Y., and Flannery, J.G. (2011). LiGluR restores
visual responses in rodent models of inherited blindness. Mol. Ther. 19,
1212–1219.
Chen, S., and Li, W. (2012). A color-coding amacrine cell may provide a blueoff signal in a mammalian retina. Nat. Neurosci. 15, 954–956.
Cohen, E., and Sterling, P. (1990). Demonstration of cell types among cone
bipolar neurons of cat retina. Philos. Trans. R. Soc. Lond. B Biol. Sci. 330,
305–321.
Cohen, E., and Sterling, P. (1991). Microcircuitry related to the receptive field
center of the on-beta ganglion cell. J. Neurophysiol. 65, 352–359.
Collin, S.P. (2008). A web-based archive for topographic maps of retinal cell
distribution in vertebrates. Clin. Exp. Optom. 91, 85–95.
Cook, J.E. (1996). Spatial properties of retinal mosaics: an empirical evaluation
of some existing measures. Vis. Neurosci. 13, 15–30.
Crook, J.D., Peterson, B.B., Packer, O.S., Robinson, F.R., Troy, J.B., and
Dacey, D.M. (2008). Y-cell receptive field and collicular projection of parasol
ganglion cells in macaque monkey retina. J. Neurosci. 28, 11277–11291.
Crook, J.D., Davenport, C.M., Peterson, B.B., Packer, O.S., Detwiler, P.B., and
Dacey, D.M. (2009). Parallel ON and OFF cone bipolar inputs establish spatially
coextensive receptive field structure of blue-yellow ganglion cells in primate
retina. J. Neurosci. 29, 8372–8387.
Dacey, D.M. (2004). Origins of perception: retinal ganglion cell diversity and the
creation of parallel visual pathways. In The Cognitive Neurosciences, M.S.
Gazzaniga, ed. (Cambridge, MA: MIT Press).
Demb, J.B., and Singer, J.H. (2012). Intrinsic properties and functional circuitry
of the AII amacrine cell. Vis. Neurosci. 29, 51–60.
Neuron 76, October 18, 2012 ª2012 Elsevier Inc. 277
Neuron
Review
Denk, W., and Horstmann, H. (2004). Serial block-face scanning electron
microscopy to reconstruct three-dimensional tissue nanostructure. PLoS
Biol. 2, e329.
Grimes, W.N., Seal, R.P., Oesch, N., Edwards, R.H., and Diamond, J.S. (2011).
Genetic targeting and physiological features of VGLUT3+ amacrine cells. Vis.
Neurosci. 28, 381–392.
Denk, W., Briggman, K.L., and Helmstaedter, M. (2012). Structural neurobiology: missing link to a mechanistic understanding of neural computation.
Nat. Rev. Neurosci. 13, 351–358.
Hartline, H.K. (1938). The response of single optic nerve fibers of the vertebrate
eye to illumination of the retina. Am. J. Physiol. 121, 400–415.
DeVries, S.H. (2000). Bipolar cells use kainate and AMPA receptors to filter
visual information into separate channels. Neuron 28, 847–856.
Devries, S.H., and Baylor, D.A. (1997). Mosaic arrangement of ganglion cell
receptive fields in rabbit retina. J. Neurophysiol. 78, 2048–2060.
DeVries, S.H., and Schwartz, E.A. (1999). Kainate receptors mediate synaptic
transmission between cones and ‘Off’ bipolar cells in a mammalian retina.
Nature 397, 157–160.
Dowling, J.E., and Boycott, B.B. (1966). Organization of the primate retina:
electron microscopy. Proc. R. Soc. Lond. B Biol. Sci. 166, 80–111.
Dryja, T.P., McGee, T.L., Berson, E.L., Fishman, G.A., Sandberg, M.A., Alexander, K.R., Derlacki, D.J., and Rajagopalan, A.S. (2005). Night blindness
and abnormal cone electroretinogram ON responses in patients with mutations in the GRM6 gene encoding mGluR6. Proc. Natl. Acad. Sci. USA 102,
4884–4889.
Herrmann, R., Heflin, S.J., Hammond, T., Lee, B., Wang, J., Gainetdinov, R.R.,
Caron, M.G., Eggers, E.D., Frishman, L.J., McCall, M.A., and Arshavsky, V.Y.
(2011). Rod vision is controlled by dopamine-dependent sensitization of rod
bipolar cells by GABA. Neuron 72, 101–110.
Hirano, A.A., Brandstätter, J.H., and Brecha, N.C. (2005). Cellular distribution
and subcellular localization of molecular components of vesicular transmitter
release in horizontal cells of rabbit retina. J. Comp. Neurol. 488, 70–81.
Hosoya, T., Baccus, S.A., and Meister, M. (2005). Dynamic predictive coding
by the retina. Nature 436, 71–77.
Huang, L., Max, M., Margolskee, R.F., Su, H., Masland, R.H., and Euler, T.
(2003). G protein subunit G gamma 13 is coexpressed with G alpha o, G
beta 3, and G beta 4 in retinal ON bipolar cells. J. Comp. Neurol. 455, 1–10.
Hubel, D.H., and Wiesel, T.N. (1965). Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28,
229–289.
Ecker, J.L., Dumitrescu, O.N., Wong, K.Y., Alam, N.M., Chen, S.K., LeGates,
T., Renna, J.M., Prusky, G.T., Berson, D.M., and Hattar, S. (2010). Melanopsin-expressing retinal ganglion-cell photoreceptors: cellular diversity and
role in pattern vision. Neuron 67, 49–60.
Huberman, A.D., Wei, W., Elstrott, J., Stafford, B.K., Feller, M.B., and Barres,
B.A. (2009). Genetic identification of an On-Off direction-selective retinal
ganglion cell subtype reveals a layer-specific subcortical map of posterior
motion. Neuron 62, 327–334.
Eggers, E.D., and Lukasiewicz, P.D. (2011). Multiple pathways of inhibition
shape bipolar cell responses in the retina. Vis. Neurosci. 28, 95–108.
Ichinose, T., and Lukasiewicz, P.D. (2007). Ambient light regulates sodium
channel activity to dynamically control retinal signaling. J. Neurosci. 27,
4756–4764.
Euler, T., and Masland, R.H. (2000). Light-evoked responses of bipolar cells in
a mammalian retina. J. Neurophysiol. 83, 1817–1829.
Euler, T., Detwiler, P.B., and Denk, W. (2002). Directionally selective calcium
signals in dendrites of starburst amacrine cells. Nature 418, 845–852.
Famiglietti, E.V., Jr., and Kolb, H. (1975). A bistratified amacrine cell and
synaptic cirucitry in the inner plexiform layer of the retina. Brain Res. 84,
293–300.
Famiglietti, E.V., Jr., Kaneko, A., and Tachibana, M. (1977). Neuronal architecture of on and off pathways to ganglion cells in carp retina. Science 198, 1267–
1269.
Farajian, R., Pan, F., Akopian, A., Völgyi, B., and Bloomfield, S.A. (2011).
Masked excitatory crosstalk between the ON and OFF visual pathways in
the mammalian retina. J. Physiol. 589, 4473–4489.
Ichinose, T., Shields, C.R., and Lukasiewicz, P.D. (2005). Sodium channels in
transient retinal bipolar cells enhance visual responses in ganglion cells. J.
Neurosci. 25, 1856–1865.
Jackman, S.L., Babai, N., Chambers, J.J., Thoreson, W.B., and Kramer, R.H.
(2011). A positive feedback synapse from retinal horizontal cells to cone
photoreceptors. PLoS Biol. 9, e1001057.
Jacobs, G.H., Williams, G.A., Cahill, H., and Nathans, J. (2007). Emergence of
novel color vision in mice engineered to express a human cone photopigment.
Science 315, 1723–1725.
Jeon, C.J., Strettoi, E., and Masland, R.H. (1998). The major cell populations of
the mouse retina. J. Neurosci. 18, 8936–8946.
Jusuf, P.R., Haverkamp, S., and Grünert, U. (2005). Localization of glycine
receptor alpha subunits on bipolar and amacrine cells in primate retina. J.
Comp. Neurol. 488, 113–128.
Feng, G., Mellor, R.H., Bernstein, M., Keller-Peck, C., Nguyen, Q.T., Wallace,
M., Nerbonne, J.M., Lichtman, J.W., and Sanes, J.R. (2000). Imaging neuronal
subsets in transgenic mice expressing multiple spectral variants of GFP.
Neuron 28, 41–51.
Kaneko, A. (1970). Physiological and morphological identification of horizontal,
bipolar and amacrine cells in goldfish retina. J. Physiol. 207, 623–633.
Freed, M.A. (2000). Parallel cone bipolar pathways to a ganglion cell use
different rates and amplitudes of quantal excitation. J. Neurosci. 20, 3956–
3963.
Kim, I.J., Zhang, Y., Yamagata, M., Meister, M., and Sanes, J.R. (2008).
Molecular identification of a retinal cell type that responds to upward motion.
Nature 452, 478–482.
Fried, S.I., Münch, T.A., and Werblin, F.S. (2002). Mechanisms and circuitry
underlying directional selectivity in the retina. Nature 420, 411–414.
Klaassen, L.J., Sun, Z., Steijaert, M.N., Bolte, P., Fahrenfort, I., Sjoerdsma, T.,
Klooster, J., Claassen, Y., Shields, C.R., Ten Eikelder, H.M., et al. (2011).
Synaptic transmission from horizontal cells to cones is impaired by loss of
connexin hemichannels. PLoS Biol. 9, e1001107.
Gauthier, J.L., Field, G.D., Sher, A., Greschner, M., Shlens, J., Litke, A.M., and
Chichilnisky, E.J. (2009). Receptive fields in primate retina are coordinated to
sample visual space more uniformly. PLoS Biol. 7, e1000063.
Gilbert, C.D. (1992). Horizontal integration and cortical dynamics. Neuron 9,
1–13.
Kleinfeld, D., Bharioke, A., Blinder, P., Bock, D.D., Briggman, K.L., Chklovskii,
D.B., Denk, W., Helmstaedter, M., Kaufhold, J.P., Lee, W.C., et al. (2011).
Large-scale automated histology in the pursuit of connectomes. J. Neurosci.
31, 16125–16138.
Gollisch, T., and Meister, M. (2010). Eye smarter than scientists believed:
neural computations in circuits of the retina. Neuron 65, 150–164.
Kuffler, S.W. (1953). Discharge patterns and functional organization of
mammalian retina. J. Neurophysiol. 16, 37–68.
Greenberg, K.P., Pham, A., and Werblin, F.S. (2011). Differential targeting of
optical neuromodulators to ganglion cell soma and dendrites allows dynamic
control of center-surround antagonism. Neuron 69, 713–720.
Lagali, P.S., Balya, D., Awatramani, G.B., Münch, T.A., Kim, D.S., Busskamp,
V., Cepko, C.L., and Roska, B. (2008). Light-activated channels targeted to ON
bipolar cells restore visual function in retinal degeneration. Nat. Neurosci. 11,
667–675.
Grimes, W.N., Zhang, J., Graydon, C.W., Kachar, B., and Diamond, J.S.
(2010). Retinal parallel processors: more than 100 independent microcircuits
operate within a single interneuron. Neuron 65, 873–885.
278 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
Lee, S., and Zhou, Z.J. (2006). The synaptic mechanism of direction selectivity
in distal processes of starburst amacrine cells. Neuron 51, 787–799.
Neuron
Review
Levick, W.R. (1967). Receptive fields and trigger features of ganglion cells in
the visual streak of the rabbits retina. J. Physiol. 188, 285–307.
Neitz, J., and Neitz, M. (2011). The genetics of normal and defective color
vision. Vision Res. 51, 633–651.
Li, W., and DeVries, S.H. (2006). Bipolar cell pathways for color and luminance
vision in a dichromatic mammalian retina. Nat. Neurosci. 9, 669–675.
Nelson, R. (1982). AII amacrine cells quicken time course of rod signals in the
cat retina. J. Neurophysiol. 47, 928–947.
Lichtman, J.W., and Sanes, J.R. (2008). Ome sweet ome: what can the
genome tell us about the connectome? Curr. Opin. Neurobiol. 18, 346–353.
Nelson, R., and Kolb, H. (1985). A17: a broad-field amacrine cell in the rod
system of the cat retina. J. Neurophysiol. 54, 592–614.
Lin, B., and Masland, R.H. (2006). Populations of wide-field amacrine cells in
the mouse retina. J. Comp. Neurol. 499, 797–809.
Nelson, R., Famiglietti, E.V., Jr., and Kolb, H. (1978). Intracellular staining
reveals different levels of stratification for on- and off-center ganglion cells in
cat retina. J. Neurophysiol. 41, 472–483.
Lin, B., Martin, P.R., Solomon, S.G., and Grünert, U. (2000). Distribution
of glycine receptor subunits on primate retinal ganglion cells: a quantitative
analysis. Eur. J. Neurosci. 12, 4155–4170.
Nobles, R.D., Zhang, C., Müller, U., Betz, H., and McCall, M.A. (2012).
Selective glycine receptor a2 subunit control of crossover inhibition between
the on and off retinal pathways. J. Neurosci. 32, 3321–3332.
Lin, B., Koizumi, A., Tanaka, N., Panda, S., and Masland, R.H. (2008). Restoration of visual function in retinal degeneration mice by ectopic expression
of melanopsin. Proc. Natl. Acad. Sci. USA 105, 16009–16014.
Ölveczky, B.P., Baccus, S.A., and Meister, M. (2003). Segregation of object
and background motion in the retina. Nature 423, 401–408.
Ma, Y.P., Cui, J., and Pan, Z.H. (2005). Heterogeneous expression of voltagedependent Na+ and K+ channels in mammalian retinal bipolar cells. Vis. Neurosci. 22, 119–133.
Peichl, L., and González-Soriano, J. (1994). Morphological types of horizontal
cell in rodent retinae: a comparison of rat, mouse, gerbil, and guinea pig. Vis.
Neurosci. 11, 501–517.
MacNeil, M.A., and Masland, R.H. (1998). Extreme diversity among amacrine
cells: implications for function. Neuron 20, 971–982.
Peichl, L., Sandmann, D., Boycott, B.B., Chalupa, L.M., and Finlay, B.L. (1998).
Comparative anatomy and function of mammalian horizontal cells. Paper presented at: Development and Organization of the Retina: From Molecules to
Function (New York: Plenum Press).
MacNeil, M.A., Heussy, J.K., Dacheux, R.F., Raviola, E., and Masland, R.H.
(1999). The shapes and numbers of amacrine cells: matching of photofilled
with Golgi-stained cells in the rabbit retina and comparison with other
mammalian species. J. Comp. Neurol. 413, 305–326.
MacNeil, M.A., Heussy, J.K., Dacheux, R.F., Raviola, E., and Masland, R.H.
(2004). The population of bipolar cells in the rabbit retina. J. Comp. Neurol.
472, 73–86.
Maddox, D.M., Vessey, K.A., Yarbrough, G.L., Invergo, B.M., Cantrell, D.R.,
Inayat, S., Balannik, V., Hicks, W.L., Hawes, N.L., Byers, S., et al. (2008). Allelic
variance between GRM6 mutants, Grm6nob3 and Grm6nob4 results in differences in retinal ganglion cell visual responses. J. Physiol. 586, 4409–4424.
Masland, R.H. (2001). The fundamental plan of the retina. Nat. Neurosci. 4,
877–886.
Masland, R.H. (2003). Vision: The retina’s fancy tricks. Nature 423, 387–388.
Masland, R.H. (2012). The tasks of amacrine cells. Vis. Neurosci. 29, 3–9.
Masland, R.H., and Martin, P.R. (2007). The unsolved mystery of vision. Curr.
Biol. 17, R577–R582.
Masland, R.H., Mills, J.W., and Cassidy, C. (1984). The functions of acetylcholine in the rabbit retina. Proc. R. Soc. Lond. B Biol. Sci. 223, 121–139.
Matsui, K., Hosoi, N., and Tachibana, M. (1998). Excitatory synaptic transmission in the inner retina: paired recordings of bipolar cells and neurons of the
ganglion cell layer. J. Neurosci. 18, 4500–4510.
Maturana, H.R., Lettvin, J.Y., McCulloch, W.S., and Pitts, W.H. (1960).
Anatomy and physiology of vision in the frog (Rana pipiens). J. Gen. Physiol.
43 (6, Suppl), 129–175.
Menger, N., Pow, D.V., and Wässle, H. (1998). Glycinergic amacrine cells of the
rat retina. J. Comp. Neurol. 401, 34–46.
Molnar, A., Hsueh, H.A., Roska, B., and Werblin, F.S. (2009). Crossover inhibition in the retina: circuitry that compensates for nonlinear rectifying synaptic
transmission. J. Comput. Neurosci. 27, 569–590.
Morgans, C.W., Zhang, J., Jeffrey, B.G., Nelson, S.M., Burke, N.S., Duvoisin,
R.M., and Brown, R.L. (2009). TRPM1 is required for the depolarizing light
response in retinal ON-bipolar cells. Proc. Natl. Acad. Sci. USA 106, 19174–
19178.
Müller, B., and Peichl, L. (1993). Horizontal cells in the cone-dominated
tree shrew retina: morphology, photoreceptor contacts, and topographical
distribution. J. Neurosci. 13, 3628–3646.
Münch, T.A., da Silveira, R.A., Siegert, S., Viney, T.J., Awatramani, G.B., and
Roska, B. (2009). Approach sensitivity in the retina processed by a multifunctional neural circuit. Nat. Neurosci. 12, 1308–1316.
Polosukhina, A., Litt, J., Tochitsky, I., Nemargut, J., Sychev, Y., De Kouchkovsky, I., Huang, T., Borges, K., Trauner, D., Van Gelder, R.N., and Kramer, R.H.
(2012). Photochemical restoration of visual responses in blind mice. Neuron
75, 271–282.
Ratliff, C.P., Borghuis, B.G., Kao, Y.H., Sterling, P., and Balasubramanian, V.
(2010). Retina is structured to process an excess of darkness in natural
scenes. Proc. Natl. Acad. Sci. USA 107, 17368–17373.
Reese, B.E., Keeley, P.W., Lee, S.C., and Whitney, I.E. (2011). Developmental
plasticity of dendritic morphology and the establishment of coverage and
connectivity in the outer retina. Dev. Neurobiol. Published online November
8, 2011. http://dx.doi.org/10.1002/dneu.20903.
Rockhill, R.L., Euler, T., and Masland, R.H. (2000). Spatial order within but not
between types of retinal neurons. Proc. Natl. Acad. Sci. USA 97, 2303–2307.
Rockhill, R.L., Daly, F.J., MacNeil, M.A., Brown, S.P., and Masland, R.H.
(2002). The diversity of ganglion cells in a mammalian retina. J. Neurosci. 22,
3831–3843.
Roska, B., and Werblin, F. (2001). Vertical interactions across ten parallel,
stacked representations in the mammalian retina. Nature 410, 583–587.
Roska, B., and Werblin, F. (2003). Rapid global shifts in natural scenes block
spiking in specific ganglion cell types. Nat. Neurosci. 6, 600–608.
Sandell, J.H., Masland, R.H., Raviola, E., and Dacheux, R.F. (1989). Connections of indoleamine-accumulating cells in the rabbit retina. J. Comp. Neurol.
283, 303–313.
Saszik, S., and DeVries, S.H. (2012). A mammalian retinal bipolar cell uses both
graded changes in membrane voltage and all-or-nothing Na+ spikes to
encode light. J. Neurosci. 32, 297–307.
Schiller, P.H., Sandell, J.H., and Maunsell, J.H. (1986). Functions of the ON and
OFF channels of the visual system. Nature 322, 824–825.
Schmidt, T.M., Do, M.T., Dacey, D., Lucas, R., Hattar, S., and Matynia, A.
(2011). Melanopsin-positive intrinsically photosensitive retinal ganglion cells:
from form to function. J. Neurosci. 31, 16094–16101.
Schwartz, G., Taylor, S., Fisher, C., Harris, R., and Berry, M.J., 2nd. (2007).
Synchronized firing among retinal ganglion cells signals motion reversal.
Neuron 55, 958–969.
Seung, H.S. (2009). Reading the book of memory: sparse sampling versus
dense mapping of connectomes. Neuron 62, 17–29.
Shen, Y., Heimel, J.A., Kamermans, M., Peachey, N.S., Gregg, R.G., and
Nawy, S. (2009). A transient receptor potential-like channel mediates synaptic
transmission in rod bipolar cells. J. Neurosci. 29, 6088–6093.
Neuron 76, October 18, 2012 ª2012 Elsevier Inc. 279
Neuron
Review
Sher, A., and DeVries, S.H. (2012). A non-canonical pathway for mammalian
blue-green color vision. Nat. Neurosci. 15, 952–953.
Siegert, S., Scherf, B.G., Del Punta, K., Didkovsky, N., Heintz, N., and Roska,
B. (2009). Genetic address book for retinal cell types. Nat. Neurosci. 12, 1197–
1204.
Sivyer, B., Taylor, W.R., and Vaney, D.I. (2010). Uniformity detector retinal
ganglion cells fire complex spikes and receive only light-evoked inhibition.
Proc. Natl. Acad. Sci. USA 107, 5628–5633.
Vaney, D.I., Sivyer, B., and Taylor, W.R. (2012). Direction selectivity in the
retina: symmetry and asymmetry in structure and function. Nat. Rev. Neurosci.
13, 194–208.
Venkataramani, S., and Taylor, W.R. (2010). Orientation selectivity in rabbit
retinal ganglion cells is mediated by presynaptic inhibition. J. Neurosci. 30,
15664–15676.
Wässle, H., and Riemann, H.J. (1978). The mosaic of nerve cells in the
mammalian retina. Proc. R. Soc. Lond. B Biol. Sci. 200, 441–461.
Sivyer, B., Venkataramani, S., Taylor, W.R., and Vaney, D.I. (2011). A novel
type of complex ganglion cell in rabbit retina. J. Comp. Neurol. 519, 3128–
3138.
Wässle, H., Peichl, L., and Boycott, B.B. (1981). Dendritic territories of cat
retinal ganglion cells. Nature 292, 344–345.
Strettoi, E., Dacheux, R.F., and Raviola, E. (1990). Synaptic connections of rod
bipolar cells in the inner plexiform layer of the rabbit retina. J. Comp. Neurol.
295, 449–466.
Wässle, H., Puller, C., Müller, F., and Haverkamp, S. (2009). Cone contacts,
mosaics, and territories of bipolar cells in the mouse retina. J. Neurosci. 29,
106–117.
Strettoi, E., Raviola, E., and Dacheux, R.F. (1992). Synaptic connections of the
narrow-field, bistratified rod amacrine cell (AII) in the rabbit retina. J. Comp.
Neurol. 325, 152–168.
Werblin, F.S. (2010). Six different roles for crossover inhibition in the retina:
correcting the nonlinearities of synaptic transmission. Vis. Neurosci. 27, 1–8.
Strettoi, E., Dacheux, R.F., and Raviola, E. (1994). Cone bipolar cells as interneurons in the rod pathway of the rabbit retina. J. Comp. Neurol. 347, 139–149.
Werblin, F.S., and Dowling, J.E. (1969). Organization of the retina of the mudpuppy, Necturus maculosus. II. Intracellular recording. J. Neurophysiol. 32,
339–355.
Tauchi, M., and Masland, R.H. (1984). The shape and arrangement of the
cholinergic neurons in the rabbit retina. Proc. R. Soc. Lond. B Biol. Sci. 223,
101–119.
Wright, L.L., and Vaney, D.I. (2000). The fountain amacrine cells of the rabbit
retina. Vis. Neurosci. 17, 1145R–1156R.
Taylor, W.R., and Smith, R.G. (2011). Trigger features and excitation in the
retina. Curr. Opin. Neurobiol. 21, 672–678.
Wu, S.M., Gao, F., and Maple, B.R. (2001). Integration and segregation of
visual signals by bipolar cells in the tiger salamander retina. Prog. Brain Res.
131, 125–143.
Van Essen, D.C. (2004). Organization of visual areas in macaque and human
cerebral cortex. In The Visual Neurosciences, J.S. Werner, L.M. Chalupa,
and C. Barnstable, eds. (Cambridge, MA: MIT Press).
van Genderen, M.M., Bijveld, M.M., Claassen, Y.B., Florijn, R.J., Pearring,
J.N., Meire, F.M., McCall, M.A., Riemslag, F.C., Gregg, R.G., Bergen, A.A.,
and Kamermans, M. (2009). Mutations in TRPM1 are a common cause of
complete congenital stationary night blindness. Am. J. Hum. Genet. 85,
730–736.
van Wyk, M., Taylor, W.R., and Vaney, D.I. (2006). Local edge detectors:
a substrate for fine spatial vision at low temporal frequencies in rabbit retina.
J. Neurosci. 26, 13250–13263.
van Wyk, M., Wässle, H., and Taylor, W.R. (2009). Receptive field properties of
ON- and OFF-ganglion cells in the mouse retina. Vis. Neurosci. 26, 297–308.
Vaney, D.I. (1991). The mosaic of amacrine cells in the mammalian retina. Prog.
Retin. Eye Res. 9, 49–100.
Vaney, D.I., and Young, H.M. (1988). GABA-like immunoreactivity in cholinergic amacrine cells of the rabbit retina. Brain Res. 438, 369–373.
280 Neuron 76, October 18, 2012 ª2012 Elsevier Inc.
Yonehara, K., Shintani, T., Suzuki, R., Sakuta, H., Takeuchi, Y., Nakamura-Yonehara, K., and Noda, M. (2008). Expression of SPIG1 reveals development of
a retinal ganglion cell subtype projecting to the medial terminal nucleus in the
mouse. PLoS One 3, e1533.
Yonehara, K., Ishikane, H., Sakuta, H., Shintani, T., Nakamura-Yonehara, K.,
Kamiji, N.L., Usui, S., and Noda, M. (2009). Identification of retinal ganglion
cells and their projections involved in central transmission of information about
upward and downward image motion. PLoS ONE 4, e4320.
Zeck, G.M., Xiao, Q., and Masland, R.H. (2005). The spatial filtering properties
of local edge detectors and brisk-sustained retinal ganglion cells. Eur. J. Neurosci. 22, 2016–2026.
Zenisek, D., Henry, D., Studholme, K., Yazulla, S., and Matthews, G. (2001).
Voltage-dependent sodium channels are expressed in nonspiking retinal
bipolar neurons. J. Neurosci. 21, 4543–4550.
Zhang, Y., Kim, I.J., Sanes, J.R., and Meister, M. (2012). The most numerous
ganglion cell type of the mouse retina is a selective feature detector. Proc. Natl.
Acad. Sci. USA 109, E2391–E2398.
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