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Contributions of Retinal
Ganglion Cells to Subcortical
Visual Processing and Behaviors
Onkar S. Dhande,∗ Benjamin K. Stafford,
Jung-Hwan A. Lim, and Andrew D. Huberman
Neurosciences Department, Neurobiology Section in the Division of Biological Sciences,
and Department of Ophthalmology, University of California, San Diego, La Jolla,
California 92093; email: [email protected], [email protected]
Annu. Rev. Vis. Sci. 2015. 1:291–328
Keywords
The Annual Review of Vision Science is online at
vision.annualreviews.org
retina, cell types, parallel pathways, vision, behavior
This article’s doi:
10.1146/annurev-vision-082114-035502
Abstract
c 2015 by Annual Reviews.
Copyright All rights reserved
∗
Corresponding author
Every aspect of visual perception and behavior is built from the neural activity of retinal ganglion cells (RGCs), the output neurons of the eye. Here, we
review progress toward understanding the many types of RGCs that communicate visual signals to the brain, along with the subcortical brain regions
that use those signals to build and respond to representations of the outside
world. We emphasize recent progress in the use of mouse genetics, viral
circuit tracing, and behavioral psychophysics to define and map the various
RGCs and their associated networks. We also address questions about the
homology of RGC types in mice and other species including nonhuman primates and humans. Finally, we propose a framework for understanding RGC
typology and for highlighting the relationship between RGC type-specific
circuitry and the processing stations in the brain that support and give rise
to the perception of sight.
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INTRODUCTION
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For mammals, including humans, the eyes are the sole entry point for visual information to reach
the brain. The retina—the thin sheet of light-sensing neural tissue at the back of the eye—is a true
natural wonder; it is about as thick as a credit card and yet contains all the necessary machinery
for converting light into a rich array of electrical signals that the brain can understand and unpack
to create visual percepts (Azeredo da Silveira & Roska 2011). The brain then uses these signals to
create internal representations of the location and qualities of visual features in the environment.
The retina consists of five layers: three layers of cells and two layers of synaptic connections,
called plexiform layers. The cell layer closest to the lens contains neurons called retinal ganglion
cells (RGCs); RGC axons exit the eye to form the optic nerve, which connects with the brain
(Figure 1a). The electrical signals carried by these RGC axons establish the crucial first link
between the outside world and our internal perception of sight.
A large amount of experimental attention has been dedicated to understanding the quality and
type of visual signals encoded by the retina and communicated to the brain by RGCs. Indeed,
progress in this area has been substantial, especially for the mouse. During the past decade,
researchers have combined classic anatomical and electrophysiological tools with genetic tools to
create a systematic and detailed exploration of both the number and type of RGCs that exist, as
well as brain areas with which they communicate. Detailed maps of the polysynaptic circuits driven
by specific types of mouse RGCs (e.g., Cruz-Martı́n et al. 2014), and causal tests of the specific
roles they play in visual processing (e.g., Chen et al. 2011), are starting to reveal the organizational
logic of the early visual pathway in this species. The hope and expectation is that the development
and application of similar sets of tools in primate species will allow the field of visual neuroscience
to develop an even stronger mechanistic understanding of human vision, as nonhuman primates
have eyes and brains that are so similar to those of humans.
In the following sections, we discuss the various criteria by which RGCs are categorized into
distinct types and subtypes, as well as key tools and information that are still lacking for RGC
typing. We also focus on the various subcortical structures to which RGCs connect, and we
discuss how those structures process retinal information to influence different light-dependent
perceptions and behaviors.
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→
Figure 1
The retina is a layered structure consisting of five neural cell types. (a) Rod and cone photoreceptors in the
outer nuclear layer signal the arrival of photons by modulating their glutamate release onto the dendrites of
bipolar cells in the outer plexiform layer. Glutamate released by photoreceptors is further modulated via
feedback and feedforward inhibition from horizontal cells. Bipolar cells, whose cell bodies reside in the inner
nuclear layer, transmit input they receive from photoreceptors by releasing glutamate onto a complex
network of ganglion cell and amacrine cell dendrites in the inner plexiform layer. Most amacrine cells are
axonless and modulate the response of ganglion cells via feedback and feedforward mechanisms. Ganglion
cells are the sole output neuron of the retina, and their axons form the optic nerve, which transmits
information to visual processing centers in the brain. (b) The cells and circuits in the retina can be divided
further on the basis of their functional output. This segregation occurs at the first synapse between
photoreceptors and bipolar cells. Bipolar cells that transmit an On signal express what are called
sign-inverting metabotropic glutamate receptors, which convert the excitatory glutamatergic signal from the
photoreceptors into an inhibitory one. Those that transmit an Off signal express conventional ionotropic
glutamate receptors and are excited by glutamate release from photoreceptors. This functional segregation
also has an anatomical correlate: The axon terminals of On and Off bipolar cells stratify in different halves of
the inner plexiform layer. Because ganglion cells typically inherit the functional polarity (On versus Off ) of
the bipolar cells that innervate them, their function can be predicted on the basis of where in the inner
plexiform layer their dendrites stratify. Abbreviation: mGluR6, metabotropic glutamate receptor.
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a
Cone
Layer
Rod
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Outer
nuclear
Outer
plexiform
Inner
nuclear
Bipolar
cell
Horizontal
cell
Inner
plexiform
Amacrine
cell
Ganglion
cell
Ganglion cell
Optic nerve
b
On
pathway
Off
pathway
Sign-inverting
mGluR6 synapse
Conventional
ionotropic synapse
On bipolar
cell
Off bipolar
cell
Functional
polarity
Off
On
1s
1s
On
ganglion
cell
Light-on response
Off
ganglion
cell
Light-off response
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ORGANIZATIONAL LOGIC OF THE MAJOR RETINAL
CELL TYPES AND CIRCUITS
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Light Conversion and Signal Transduction in the Retina
Processing of light information occurs first in the outer retina, where photoreceptors—rods and
cones—absorb photons in their outer segments and, by using various opsin transduction cascades
(reviewed in Korenbrot 2012), convert those photons into electrical signals that the rest of the
nervous system can understand. Rods are highly sensitive to photons and operate under low-light,
or scotopic, conditions, whereas cones absorb light only at brighter, or photopic, levels. Rods
and cones synapse onto their postsynaptic partners, bipolar and horizontal cells. The details of
the photoreceptor-to-target-cell communication are presented schematically in Figure 1 and are
described in detail elsewhere (Demb & Singer 2015).
For purposes of this review, it suffices to say that the presence of light in the visual field reduces
glutamate release from rods and cones. This reduction in glutamate release effectively excites
bipolar cells expressing metabotropic sign-inverting glutamate receptors (On bipolar cells) by
removing inhibition and causes these cells to release glutamate from their terminals in the inner
plexiform layer (IPL) (Figure 1b). In contrast, the presence of darkness triggers photoreceptors
to increase glutamate release, thereby exciting bipolar cells that express conventional ionotropic
glutamate receptors (Off bipolar cells) (Figure 1b). Horizontal cells, which are GABAergic and
inhibitory, shape the flow of photoreceptor-to-bipolar-cell information through feedback and
feedforward mechanisms (reviewed in Thoreson & Mangel 2012).
As mentioned above, bipolar cells are excitatory glutamatergic neurons. They fall into two
general categories: rod bipolar cells and cone bipolar cells, depending on their presynaptic inputs.
In the mouse, there are at least 13 different types of bipolar cells that have been described morphologically, and each type stratifies its axon terminals at a distinct laminar depth in the inner retina
(Figure 2a). Further, the response properties of bipolar cells differ among types, and bipolar cells
impart these functional properties (e.g., On versus Off, sustained versus transient) upon the RGCs
with which they communicate. In this way, bipolar cell terminals establish the first anatomical
laminar segregation of parallel pathways for unique feature detection in the visual system. Indeed,
this segregation dictates, in large part, the response properties of the different RGCs. As a general
rule, RGCs stratify their dendrites at the same point in the IPL as the axon terminals of the bipolar
cell type(s) from which they receive input. There is one known exception in the mouse, however:
an Off stratifying ganglion cell that receives input from On bipolar cells via en passant synapses
and, therefore, is functionally an On RGC (Figure 2b). Whether additional exceptions exist in
the mouse or in other species remains to be seen.
Lateral filtering of visual signals at the level of bipolar cell output is accomplished by a specific
class of interneurons, the amacrine cells. More than three dozen types of amacrine cells have been
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→
Figure 2
Origins of parallel pathways in the retina. (a) Bipolar cells can be divided into two broad types: rod and cone bipolar cells. There is only
one type of rod bipolar cell ( green), an On type, that stratifies its axon terminals in the extreme vitreal portion of the inner plexiform
layer (IPL). There are at least 13 types of bipolar cells. The axon terminals of cells that carry Off signals ( purple) stratify in the scleral half
of the IPL, whereas those of cells that carry On signals (blue) stratify in the vitreal half. Figure adapted with permission from Euler et al.
(2014). (b) There is at least one type of Off-stratifying ganglion cell that possesses an On light response (Hattar et al. 2002). This cell
( gray) receives On pathway input in the form of en passant synapses (red box) from specific type(s) of On bipolar cell(s) (blue) (Dumitrescu
et al. 2009). (c) The extreme heterogeneity in the morphological scale of retinal cell types allows signals to be processed both locally
and over long distances in the circuits that detect and process visual information. Wide-field amacrine cell adapted from Lin et al.
(2004). Starburst amacrine cell adapted from Wei & Feller (2011). Alpha ganglion cell adapted from El-Danaf & Huberman (2015).
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identified; some types are wide-field and polyaxonal, allowing them to shape visual processing over
considerable spans of the retina, whereas others are narrow-field and thus shape retinal circuit
interactions more locally (Figure 2c). All but a few amacrine cells are inhibitory (Grimes et al.
2011, Lee et al. 2014, Krishnaswamy et al. 2015); they either release GABA or glycine (but not
both). However, many amacrine types corelease neuromodulators such as acetylcholine (Ach) or
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a
Off bipolar cells
1
2
3a
3b
On bipolar cells
4
5a
5b
XBC
6
7
Rod bipolar cell
8
9
Off
On
b
Conventional
On bipolar cell
En passant
On bipolar cell
En passant synapse
Conventional
On ganglion cell
c
Off-stratifying
On ganglion cell
Wide-field amacrine cell
Alpha
ganglion cell Starburst
amacrine cell
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dopamine (DA) along with GABA or glycine. For a thorough review of amacrine contributions to
retinal processing see the accompanying review by Demb & Singer (2015).
Ganglion Cells
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At the risk of sounding RGC-centric, the ultimate purpose of all other retinal cell types and
circuits is to pass along information about certain features of the visual world to specific types of
RGCs so that they in turn can transmit that information to the brain. It is generally accepted that
there are ∼20–30 different RGC types, and the study of how RGCs are classified into distinct
types represents a useful framework for classifying cell types elsewhere in the mammalian central
nervous system (Seung & Sümbül 2014, Sanes & Masland 2015). Indeed, RGCs offer the unique
opportunity to study all of the parameters by which neurons have been traditionally classified:
morphology, response to defined stimuli, presynaptic inputs, postsynaptic targets, and molecular
expression profiles, among others. Before embarking on a detailed description of the known RGC
types and subtypes, we briefly address how a given RGC is classified into a specific category and
how, to some extent, this procedure is now standardized.
How Are Retinal Ganglion Cell Types Defined?
RGCs are classified by several parameters, none of which is definitive of a specific type on its own.
One of the hallmark parameters is mosaicism; that is, the relatively uniform spacing of neuronal
cell bodies of cells of a given type, and therefore function, in the plane of the retina (Figure 3a).
Mosaicism makes intuitive sense because it ensures that visual information is encoded by the same
number of cells of a given functional type at any point in the visual scene. Thus, this property is
considered by many to be one of the most fundamental features for determining a unique RGC
type (reviewed in Cook 1996, Field & Chichilnisky 2007), and remains a highly useful, and readily
quantifiable metric for classifying RGC types (Cook 1996).
Another classic way by which RGC types have been characterized is by their en face
morphology, which includes two features: (a) soma size and (b) dendritic branching patterns
in the IPL. These features work well to distinguish and categorize RGC types that tend to
have very large versus very small dendritic arbors and somas (Figure 3b) (Rodieck et al. 1985).
Indeed, physiologists have used soma size and shape to bias targeting of their recordings toward
specific RGC types for decades. On its own, however, dendritic branching pattern is a somewhat
precarious feature to rely on for purposes of RGC classification. RGCs that reside within the same
mosaic, and that stratify at the same depth(s) within the IPL (and thus encode the same functional
features), can have widely different en face morphologies (Bleckert et al. 2014; reviewed in Dhande
& Huberman 2014b). These differences are especially prominent in species such as primates, in
which high-acuity regions of the retina are used for detailed processing of specific regions of the
visual scene (Watanabe & Rodieck 1989, Dacey & Peterson 1992; also see Stone 1983).
The third major feature by which RGC types are categorized is their unique patterns of dendritic stratification (Figures 3c and 4) (also see Berson 2008, Sanes & Masland 2015). This property
is observed as cell type–specific differences in the IPL depth at which they direct their dendritic
arbors. The importance of this feature for RGC typing is hard to overstate because it dictates the
identities of the presynaptic cells—bipolars or amacrines—from which the RGCs are positioned to
collect synaptic input (see, for example, Figure 2a). Indeed, new techniques focused on quantifying stratification position within the IPL are making it possible to detect differences amongst RGC
types with ever greater precision (Manookin et al. 2008, Sümbül et al. 2014). Thus, above all else,
dendritic stratification provides our first guess as to which visual features an RGC will respond best.
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b
Functional mosaics
En face morphologies
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Local object motion detectors
Spot
detectors
Luminance
detectors
c
Stratification patterns
Layer
ONL
OPL
INL
IPL
GCL
Figure 3
Organizational principles for typing ganglion cells. (a) The functional output of a ganglion cell can be used as a classification criterion.
The cell bodies of ganglion cells with homogenous functions tile the retina, forming semiuniform mosaics. (b) En face morphology,
including dendritic field size and complexity, has also been used to classify different types of ganglion cells. Black cell adapted from
Sümbül et al. (2014). Dark red cell adapted from Berson et al. (2010). Green and blue cells adapted from El-Danaf & Huberman
(2015). (c) Stratification depth within the IPL can also be used to classify cells. The dendrites of cells with specific anatomical and
functional properties stratify in highly stereotypical locations within the IPL. Abbreviations: GCL, ganglion cell layer; INL, inner
nuclear layer, IPL, inner plexiform layer; ONL, outer nuclear layer; OPL, outer plexiform layer.
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In fact, modeling data support the idea that if one had to determine RGC type on the basis of only
one morphological feature, the sine qua non feature is dendritic stratification (Sümbül et al. 2014).
A similarly new and developing method for classifying RGCs into distinct types is their signature
pattern of gene expression (Kay et al. 2011, Siegert et al. 2012). To date, the best examples of
RGC typing using this method are the melanopsin-expressing intrinsically photosensitive RGCs
(ipRGCs). As their name suggests, these RGCs can directly respond to light without the need for
rod or cone input, and, therefore, act as a class of RGC photoreceptors (Berson et al. 2002; see
below). The ipRGCs are a unique case among RGC types, however, because the expression of a
single protein—melanopsin—determines the essence of the cell type classification. Indeed, this
classification system is similar to how standard photoreceptors can be classified into distinct types
(such as L- or S-cones) based on their unique expression of opsin photopigments (Nathans 1987),
rather than on differences in, for example, morphology. Even melanopsin expression, however,
has limitations when used for classifying RGCs. As discussed in greater detail below, there are
numerous subtypes of ipRGCs which apparently express varying levels of melanopsin. Thus,
melanopsin expression alone is insufficient for classifying the various ipRGC subtypes.
Given the difficulty in finding individual genes that mark specific types of RGCs, the search for
molecular markers has centered on developmental genes, such as transcription factors, that dictate
stratification and connectivity (Xiang et al. 1996). A handful of such markers have been identified,
but virtually all of them are required in combination to faithfully represent individual RGC types;
there are very few bona fide individual markers for distinct RGC types (Table 1) (Badea et al. 2009,
Mao et al. 2014, Sweeney et al. 2014). The adhesion molecule JamB is a notable exception, as it
marks a very specific RGC type and, reportedly, does not mark any others (Kim et al. 2008, 2010).
Other markers that have been used in the past to label specific populations of RGCs, such as the
antibody SMI-32, which recognizes the nonphosphorylated form of heavy chain neurofilaments,
have recently been shown to label more than a single type of RGC; SMI-32 is expressed at low
levels in at least three or four types of RGCs (Lin et al. 2004, Coombs et al. 2006). In sum, many of
these markers are selective for specific subtypes within the RGC population but are not exclusive
to specific types of RGCs per se.
In recent years, transgenic mice harboring fluorescent reporters under the control of specific
promoter elements, as well as knockin mice with site-specific insertions of Cre and/or a fluorescent
reporter, have been used to label and study specific subtypes of RGCs with great success. Although
some of these mice come with the caveat that the transgenes may not recapitulate the endogenous
activity of the promoters in their entirety (Kay et al. 2011), these systems have allowed researchers
to address virtually all of the typing criteria described in the preceding sections (Huberman et al.
2008b, 2009; Kim et al. 2008; Yonehara et al. 2008; Ecker et al. 2010; Kay et al. 2011; Osterhout
et al. 2011; Trenholm et al. 2011; Dhande et al. 2013). In total, these mice have been used to
“genetically identify” 15 of the 20–30 RGC subtypes reported to exist in the mouse (Coombs
et al. 2006, Völgyi et al. 2009). The morphology and stratification patterns of these RGCs and
the transgenic mouse lines that mark them are shown in Figure 4. Indeed, a variety of genetically modified mice are now being used in dozens of laboratories around the world to generate
detailed knowledge about RGC type–specific physiology, morphology, central projections, gene
expression, and contributions to behavior.
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MAJOR TYPES OF RETINAL GANGLION CELLS
As mentioned above, there are an estimated 20–30 morphologically and physiologically distinct
RGC subtypes in the mouse and in primates. To parse these into distinct and well-defined groups,
it is useful to divide them into several broad functional categories, such as motion-sensitive versus
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Table 1 Molecular markers and transgenic mouse lines used to study functionally distinct retinal ganglion cell subtypes
Retinal ganglion cell
subtype
Directionselective
Icon
On–Off
DSGC
V
T
N
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Unique marker(s)
D
On DSGC
V
Transgenic line(s)
Posterior: CART,
Mmp17
Anterior: NA
Upward: CART,
Col25a1, Cdh6
Downward: CART,
Col25a1, Cdh6
Drd4-GFP
TRHR-GFP
Hoxd10-GFP
Hb9-GFP
TYW9
Cdh6-CreER
BD(FSTL4/Spig1)CreER
Huberman et al.
(2009)
Kay et al. (2011)
Rivlin-Etzion et al.
(2011)
Trenholm et al.
(2011)
Dhande et al. (2013)
Unknown
FSTL4/Spig1-GFP
Hoxd10-GFP
Yonehara et al.
(2008)
Dhande et al. (2013)
Jam-B
JAM-B-CreER
Kim et al. (2008)
SCN-projecting
Melanopsin+
Brn3b−
Opn4-TauLacZ
Opn4-GFP
Opn4-Cre
Opn4-CreER
Hattar et al. (2002)
Schmidt et al. (2008)
Ecker et al. (2010)
Chen et al. (2011)
T
N
Reference(s)
D
Off DSGC
V
N
Intrinsically
photosensitive
M1
T
OPN-projecting
Melanopsin+
Brn3b+
M2
Unknown
Melanopsin+
Brn3b+
Opn4-TauLacZ
Opn4-GFP
Opn4-Cre
Cdh3-GFP
Schmidt et al. (2008)
Ecker et al. (2010)
Osterhout et al.
(2011)
Hattar et al. (2002)
M3
Unknown
Melanopsin+
Brn3b+
Opn4-GFP
Schmidt et al. (2008)
Melanopsin+
Brn3b+
SMI32+
Opn4-Cre
Ecker et al. (2010)
Melanopsin+
Brn3b+
Opn4-Cre
Ecker et al. (2010)
Unknown
TYW3
Kim et al. (2010)
Zhang et al. (2012)
M4
M5
Object
motion
W3A
W3B
Unknown
(Continued )
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Table 1 (Continued )
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Retinal ganglion cell
subtype
Icon
Unique marker(s)
Transgenic line(s)
Reference(s)
Center–surround
(alpha)
W7A
W7B
SMI-32
Osteopontin
TYW7
CB2-GFP
KCNG4-Cre
Huberman et al.
(2008)
Kim et al. (2010)
Duan et al. (2015)
Loomingsensitive
PV-5
Unknown
PV-Cre
Münch et al. (2009)
Farrow et al. (2013)
Abbreviations: DGSC, direction-selective ganglion cell; GFP, green fluorescent protein; NA, not available; OPN, olivary pretectal nucleus;
SCN, suprachiasmatic nucleus.
non-motion-sensitive or broad versus detailed spatial feature detectors. To clarify: we refer to an
RGC type as a major category of, say, direction-selective RGCs, whereas our description of an
RGC subtype resides within a particular categorization. For example, upward-sensitive On–Off
direction-selective ganglion cells (DSGCs) represent one On–Off DSGC subtype, downwardsensitive On–Off DSGCs represent another subtype, and so on.
Non-Motion-Sensitive Retinal Ganglion Cell Types
Some of the earliest RGC types to be studied can be loosely described as spot detectors. These
types of RGCs respond to increments or decrements of light in their receptive fields and, in some
instances, possess an antagonistic surround. In the following subsections, we highlight several of
these types of RGCs that have been well characterized in the past 50–60 years.
Alpha retinal ganglion cells. One of the classic RGC types is the alpha cell. First described
functionally by Kuffler (1953) in the cat retina, and coined the “alpha” cell by Boycott & Wässle
(1974), these cells are monostratified and relatively large, both in terms of their somas and the
diameters of their dendritic arbors, making them one of the easiest RGCs to target for filling
and/or electrophysiological recordings. Alpha RGCs are center–surround spot detectors, of
which there are four subtypes that stratify at different IPL depths in the mouse and that are named
for the properties of their receptive field centers: On-transient, On-sustained, Off-transient, and
Off-sustained (Pang et al. 2003, van Wyk et al. 2009, Stafford et al. 2010). Although the number
of alpha cell subtypes varies across species, the receptive field properties of alpha RGCs are remarkably similar in cats, mice, and guinea pigs (Manookin et al. 2008, van Wyk et al. 2009), and at
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→
Figure 4
Representative morphologies and inner plexiform layer (IPL) stratification depths of genetically labeled ganglion cells in the retinas of
transgenic mouse lines. For each cell type, a representative en face morphology is shown, along with its stereotypical stratification
depth within the IPL, normalized to two fiducial strata within the IPL; IPL strata (On and Off ChAT bands) are labeled by a choline
acetyltransferase (ChAT) antibody, and their normalized positions are indicated by the dashed lines in the stratification panels. Solid
gray boxes around cells indicate multiple cell types labeled in a single transgenic line. Known functions are listed below each cell type.
M1 and M2 cells adapted from Berson et al. (2010). M3 cell adapted from Schmidt et al. (2014). M5 cell adapted from Hu et al. (2013).
M4 cell adapted from Estevez et al. (2012). W3, W7A, W7B, and K cells adapted from Sümbül et al. (2014). Jam-B cell adapted from
Kim et al. (2008). Hoxd10 and CB2 cells adapted from El-Danaf & Huberman (2015). Drd4/TRHR/W9/BDa cell based on
unpublished data from the Huberman lab. Abbreviations: DS, direction-selective; t-Off, Off-transient; s-Off, Off-sustained; s-On,
On-sustained; aOn–Off, anterior-tuned On–Off; pOn–Off, posterior-tuned On–Off; uOff, upward-tuned Off; uOn–Off, upward-tuned
On–Off.
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Opn4/Opn4-Cre
M1
Luminance
CB2
M2
Luminance
W3
t-Off alpha
Local motion
Off
On
Off
On
M5
Drd4/TRHR/W9
BDa
Off
On
pOn–Off DS
uOn–Off DS
M4
s-On alpha
M3
Off
On
uOff DS
Off
On
Hoxd10
aOn–Off DS
Jam-B
W7A/B
s-Off Delta (W7B)
On DS
Off
On
t-Off (W7A)
Off
On
Unknown
Ka
Kb
Off
On
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least one of the subtypes is likely the center–surround RGC made famous by the classic studies by
Kuffler (1953) and others in the cat retina (Barlow et al. 1957, Wiesel 1960, Rodieck & Stone 1965).
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Local edge detectors. First described physiologically in rabbits by Levick (1967), cells belonging
to this RGC type prefer stimuli restricted to their small receptive field centers and respond poorly
to large spots, full-field stimuli, or extended gratings. Although these cells respond well to small
light or dark edges, they exhibit no directional preference. Their circuit properties were later
characterized in detail in the rabbit (van Wyk et al. 2006) and, very recently, were genetically
identified in the mouse (Zhang et al. 2012). As in the rabbit, mouse local edge detectors are cells
with very small dendritic fields and On–Off responses resulting from broad stratification of their
dendrites within the central IPL, where they apparently receive mixed On and Off bipolar cell
input.
Orientation-selective retinal ganglion cells. Single-cell recordings in rabbit, cat, and primate
retinas indicate that orientation-selective RGCs are present in these species (Levick & Thibos
1982, Bloomfield 1994, Passaglia et al. 2002, Venkataramani & Taylor 2010). Multisite electrode
recordings (Zhao et al. 2013) suggest the presence of orientation-selective RGCs in the mouse
retina as well, although such cells have yet to be genetically identified or studied in detail in the
mouse at the level of morphology, circuitry, or contribution to central vision.
Intrinsically photosensitive retinal ganglion cells. These cells represent a limited subset of
ganglion cells (<2% of all RGCs) that express melanopsin, making them intrinsically photosensitive (Berson 2013). Physiologically, they display sustained responses to steady illumination and
thereby encode global light intensity levels in the ambient environment (Zhao et al. 2014b).
To date, five subtypes of ipRGCs (M1–M5) have been identified, all but one of which are
monostratified, and each of which possesses a different level of intrinsic photosensitivity attributable to varying levels of melanopsin expression (Ecker et al. 2010, Hu et al. 2013). Further, some ipRGCs are actually subsets of other subtypes of RGCs. For example, the M4s are
On-sustained alpha RGCs, although it is not known whether every On-sustained alpha RGC expresses melanopsin (Estevez et al. 2012). Nevertheless, because On-sustained alpha RGCs express
melanopsin, it is clear that some ipRGCs can play multiple roles in visual processing and are not
limited to encoding only ambient light intensity (Schmidt et al. 2014). Moreover, ipRGCs exist
in both mice and primates, and some cell types have nearly identical physiology and morphology,
with the primary difference being one of scale (Figure 5). Whether or not all five subtypes exist
in every species in which these cells have been identified, however, remains unclear (Berson et al.
2002, Hannibal et al. 2004, Dacey et al. 2005).
Motion-Sensitive Retinal Ganglion Cell Types
Certain types of RGCs respond to moving stimuli in addition to responding to increments or
decrements of light in their receptive fields. Some types of motion-sensitive RGCs respond to
stimuli moving along a particular axis of the visual field, whereas other types respond to specific
patterns of object motion. In the following subsections, we review some of the better characterized
motion-sensitive RGCs.
On–Off direction-selective retinal ganglion cells. On–Off DSGCs were first discovered by
Barlow and coworkers in the early 1960s (Barlow & Hill 1963; reviewed in Vaney et al. 2012). As
their name suggests, these cells respond to both light and dark edges moving along a particular
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b
Macaque
Mouse
Anti-Opn4
Anti-Opn4
200 μm
25 μm
20 mV
20 mV
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25 μm
50 μm
On (10 s)
c
On (10 s)
Mouse ipRGC scaled
to primate
Figure 5
Molecular markers label homogeneous populations of ganglion cells in primates and in the mouse. (a) Melanopsin-expressing ganglion
cells in the macaque retina have large dendritic trees and sustained light responses. Example en face image and physiological trace
adapted with permission from Dacey et al. (2005). Antibody staining based on unpublished data from the Huberman lab.
(b) Melanopsin-expressing ganglion cells in the mouse have the same morphological and functional properties as those in the macaque
retina, albeit on a much smaller scale. Example en face image adapted with permission from Berson et al. (2010). Antibody staining and
physiological trace based on unpublished data from the Huberman lab. (c) Inset showing a mouse melanopsin ganglion cell in panel b
drawn to scale relative to the macaque cell in panel a. Abbreviation: ipRGC, intrinsically photosensitive retinal ganglion cell.
axis of the visual field [up, down, forward (nasal), or backward (temporal)], and they can respond
to a fairly broad range of speeds (up to 60 deg/s) (Oyster et al. 1972, Weng et al. 2005, Sivyer
et al. 2010). Moreover, their morphology predicts their functionality: They are bistratified and
extend dendrites in both the On and Off layers of the IPL. The On–Off DSGCs, and the cells and
circuits that endow these cells with their direction selectivity (reviewed in Vaney et al. 2012), have
been a major focus of visual neuroscience and, indeed, stand as one of its great success stories in
defining exactly how neurons perform discrete computations.
Accessory optic system–projecting direction-selective ganglion cells. A different type of
DSGC comprises those that project to accessory optic targets in the brainstem to control imagestabilizing eye movements (reviewed in Simpson 1984; Yonehara et al. 2008, 2009; Dhande et al.
2013). These accessory optic system (AOS)-projecting DSGCs historically have been called OnDSGCs because they were believed to consist of three subtypes of monostratified cells that respond
to light edges moving along one of three axes of the visual field: upward, forward, or backward.
This term warrants revision, however, as we now know from studies in mice that AOS-projecting
RGCs include the three subtypes of monostratified On-DSGCs, along with a bistratified forwardsensitive On–Off DSGC subtype (Dhande et al. 2013) and a bistratified downward-sensitive
On–Off DSGC subtype (Kay et al. 2011). In addition, many On-DSGCs extend a fraction of
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their dendritic arbor into the Off sublamina, although this fraction is apparently not sufficient to
generate a functional Off response in these cells (Dhande et al. 2013) (see, e.g., Hoxd10-RGCs in
Figure 4). The key distinction between the AOS-projecting DSGCs and the canonical On–Off
DSGCs described above is that all AOS-projecting DSGCs are tuned for slow speeds. This tuning
makes intuitive sense given the behavioral requirements of this system: The AOS offsets the slow
drift motion of the head and eyes and allows for image stabilization on the retina. (see Dhande
et al. 2013 and movies at the following URL: http://www.hubermanlab.com/movies.html).
Off-type direction-selective ganglion cells. A monostratified, purely Off-DSGC type, called
a J-RGC, was discovered recently in the mouse (Kim et al. 2008). These J-RGCs are named
for their expression of the cell adhesion molecule JamB and are unusual among DSGCs for two
reasons: (a) They respond best to upward motion of dark stimuli, and (b) their directional tuning
properties are determined by their fanlike dendritic shape and by their orientation with respect
to the retinal axes (Kim et al. 2008, 2014), rather than by assymetric inhibition from starburst
amacrine cells, which generates direction selectivity in the canonical On–Off DSGCs and the
AOS-projecting RGCs (Briggman et al. 2011, Yonehara et al. 2011).
Object motion–sensitive retinal ganglion cells. A remarkable type of RGC comprises the
object motion–sensitive (OMS) cells. These cells do not respond to motion in a specific direction;
instead, they respond to differential motion. Broadly defined, differential motion occurs when a
pattern within the receptive field of a given cell moves differently from a pattern in its surround.
These cells are the “bug detectors” described by Lettvin and coworkers (1959) in their classic
treatise “What the Frog’s Eye Tells the Frog’s Brain.” The circuitry underlying these cells was
later worked out in both salamander and rabbit retinas, although it is not clear whether OMS
cells represent a pure RGC type or include several types and/or subtypes (Ölveczky et al. 2003).
For example, it is possible that the LED cells described above are a subtype of OMS RGCs.
Nevertheless, the ethological significance of these cells is apparent and underscores the extent to
which RGCs can inform the brain about stimuli far richer than just spots and moving bars.
Looming-sensitive retinal ganglion cells. Other monostratified, Off RGCs in the mouse retina
have been reported to encode looming objects, particularly dark, expanding objects overhead
(Münch et al. 2009). Interestingly, the functional properties and stratification patterns of these
RGCs strongly resemble those of Off-transient alpha RGCs, which have also been described in
the mouse (Pang et al. 2003, Huberman et al. 2008b). However, whether or not they are a different
set of RGCs altogether remains unclear.
Specialized Retinal Ganglion Cell Types of the Primate
The primate retina possesses several specializations that mouse, rat, and rabbit retinas lack. First
and foremost, the primate retina has a fovea, that is, an area devoted to high-acuity vision. Second,
within the fovea, RGC types that are unique to the primate help generate the high-acuity vision
subserved by this region. Finally, RGCs of the primate retina exhibit robust color opponency.
In the following subsections, we discuss some of these primate-specific RGC types and, where
possible, attempt to draw parallels with RGC types of other species.
Midget retinal ganglion cells in primates. Midget RGCs are the most numerous type in the
primate retina. These cells are monostratified, consist of two subtypes—On and Off—depending
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upon where they stratify their dendrites, have very small receptive fields, and carry chromatic
information to the brain. Although comparisons have been drawn between the midget RGCs
of primates and the X (sometimes called beta) RGCs of the cat retina, the primary relationship
between these two groups is that, relatively speaking, each type represents the highest-acuity
system in its species. As such, the midget RGC may be a primate specialization designed for highacuity red–green color-opponent vision (Watanabe & Rodieck 1989, Dacey & Petersen 1992).
Small bistratified retinal ganglion cells. These cells have been documented in various primate
species (marmoset, macaque), in which they carry yellow–blue opponent signals to the koniocellular layers of the dorsal lateral geniculate nucleus (dLGN) (Field et al. 2007, Szmajda et al. 2008;
reviewed in Hendry & Reid 2000). They have been hypothesized to be homologous to the general
group of so-called W (also called gamma) cells in cats. Given that the W or gamma type classification encompasses many different RGC types, however, this comparison is not exact (Berson
2008). For a review of the morphologies of the various non-parasol, non-midget, non-yellow–blue
color-opponent RGCs in monkeys, the reader is referred to an article by Dacey (2004).
Parasol cells. Parasol cells are monostratified RGCs with medium-sized dendritic trees and
comprise two subtypes—On and Off—that vary depending on where they stratify their dendrites
in the IPL. Some researchers have considered alpha RGCs to be homologous to the parasol cells of
the primate retina (Dacey & Petersen 1992, Crook et al. 2008). Others, however, contest this
notion, suggesting instead that the primate upsilon cell is the alpha homolog, and the parasols
are a unique type altogether (Petrusca et al. 2007). The hope is that by extending the molecular
techniques being developed to classify mouse RGC types into the primate retina, and combining
them with our expanding knowledge of primate RGC physiology, will help resolve the parasol
versus upsilon/alpha debate.
The above list of RGC types is not intended to be exhaustive; surely additional types and
subtypes of RGCs are still yet to be found. In the next section, we review progress in understanding
where in the brain the different types of information encoded by RGCs are routed, and the ways
in which the brain targets of RGC information streams use the information received.
CENTRAL PROJECTIONS OF RETINAL GANGLION CELLS
The retinofugal pathway contains ∼46 different target structures, each of which receives direct
input from RGCs (Morin & Studholme 2014). Some of these RGC targets are well known, such as
the dLGN, which relays visual information to the cortex for conscious perception of visual scenes.
Other targets, such as the amygdala and habenula, are less well known as being retinorecipient
nuclei, and their roles in visual processing remain poorly understood (Hattar et al. 2006, Morin
& Studholme 2014). In the following subsections, we review several of the major parallel optic
pathways and targets whose functions are generally understood and for which detailed knowledge
about the type and role of RGC input is starting to emerge (Figure 6). For the sake of simplicity,
we divide the optic pathway into three general categories: (a) subcortical visual circuits that govern
the whole-animal physiological state, such as the retinofugal pathways governing entrainment of
circadian rhythms; (b) circuits that control visually driven reflexive behaviors, such as the pupillary
light reflex (PLR) and eye movements for stabilizing images on the retina; and (c) circuits for
encoding complex visual features in the superior colliculus (SC) and dLGN.
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SC
mdPPN
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NOT/DTN
OPN
Habenula
dLGN
IGL
vLGN
MTNd
Zona
incerta
SCN
MTNv
Amygdala
On–Off DSGCs
Fast-tuned
BD
TRHR
Drd4
Slow-tuned
On DSGCs
Hoxd10
Hoxd10/Spig-1
ipRGCs (M1-5)
Opn4/Opn4-Cre/Cdh3
M1
M2
M3
M4
Off DSGCs Object motion
Jam-B
W3
t-Off Alpha
s-Off
CB2
W7A
M5
Figure 6
Schematic showing projection patterns of genetically labeled ganglion cell types to subcortical visual targets
for all transgenic mouse lines in which one or a small number of ganglion cells are labeled. For simplicity,
axonal projections of melanopsin-expressing ganglion cells have been shown as a single population.
Abbreviations: dLGN, dorsal lateral geniculate nucleus; DSGCs, direction-selective ganglion cells; IGL,
intergeniculate leaflet; ipRGCs, intrinsically photosensitive retinal ganglion cells; mdPPN, medial division
of the posterior pretectal nucleus; MTNd, dorsal medial terminal nucleus; MTNv, ventral medial terminal
nucleus; NOT/DTN, nucleus of the optic tract/dorsal terminal nucleus; OPN, olivary pretectal nucleus; SC,
superior colliculus; SCN, suprachiasmatic nucleus; s-Off, Off-sustained; t-Off, Off-transient; vLGN, ventral
lateral geniculate nucleus.
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Setting the Brain’s Clock: A Retinofugal Circuit Governing Entrainment
of Endogenous Circadian Rhythms
The suprachiasmatic nucleus (SCN) is the master circadian clock in the brain; it synchronizes
endogenous rhythms to a 24-hour cycle and is coupled to the ambient light–dark cycle via RGC
inputs (reviewed in Welsh et al. 2010, Lucas et al. 2012, Morin 2013, Legates et al. 2014). The
SCN is the first retinorecipient target in the optic pathway. It consists of two densely packed,
bilateral nuclei situated above the optic chiasm, each of which receives direct synaptic input via
the retinohypothalmic tract. Just over a decade ago, it was discovered that the axons of melanopsinexpressing ipRGCs form the predominant projection pathway to the SCN (Gooley et al. 2001;
Berson et al. 2002; Hattar et al. 2002, 2006) (Figure 7a). In early studies, multiple research
groups took advantage of the fact that so-called intrinsic photosensitivity is a direct consequence
of melanopsin expression in these RGCs and therefore generated mice that (a) express a marker
(LacZ) from the melanopsin locus and (b) lack the melanopsin gene. Their results revealed the
direct projection from melanopsin-expressing RGCs to the SCN and provided direct evidence
that melanopsin signaling is involved in the photoentrainment of circadian rhythms (Panda et al.
2002, Ruby et al. 2002, Hattar et al. 2003).
Although these studies demonstrated an important role for melanopsin signaling in entrainment to light–dark cycles, circadian photoentrainment was not completely abolished in melanopsin
knockout mice. This finding suggests that although RGCs that express melanopsin may be essential for circadian photoentrainment, the same RGCs may also influence photoentrainment via
signals arising from the classic rod–cone pathway. The alternative hypothesis is that RGCs other
than the melanopsin-expressing RGCs are also important for entrainment. Support for the idea
that ipRGCs are the major drivers in this system came in the form of three studies published
in 2008 (Göz et al. 2008, Güler et al. 2008, Hatori et al. 2008), each of which independently
demonstrated that specifically killing melanopsin-expressing RGCs resulted in a complete loss of
circadian photoentrainment. These articles remain landmark studies because they are the first to
directly link a specific RGC subtype (defined by its morphology, electrophysiological properties,
connections with the brain, and gene expression) to a clear and defined subset of light-driven
behaviors.
Although the importance of ipRGCs in entraining the master clock in the SCN is now
well established, several important questions remain unresolved. For example: How does each
of the different ipRGC subtypes contribute to photoentrainment? The SCN receives input
predominantly from M1 and M2 ipRGCs, and they appear to innervate distinct subregions of the
SCN (Abrahamson & Moore 2001, Baver et al. 2008, Ecker et al. 2010). However, the functional
significance of this specific innervation pattern remains unclear. In terms of the identities of
the SCN-projecting ipRGCs versus non-SCN-projecting ipRGCs, Chen et al. (2011) elegantly
showed that the M1 ipRGCs that lack the transcription factor Brn3b target the SCN, whereas
Brn3b+ M1 ipRGCs target other regions of the visual system (see the subsection titled “The
Pupillary Light Reflex: A Circuit for Ensuring that Appropriate Light Levels Reach the Brain”
and Figure 7). Thus, it is clear that even genetically unique populations within ipRGC subtypes
have important roles in driving these behaviors.
The retino–ventral thalamic pathway helps modulate nonphotic circadian function.
The retino-SCN projection pathway is indispensable for resetting the phase of the circadian
pacemaker. However, retinal pathways that indirectly drive the SCN have also been implicated in
entraining circadian rhythms, mainly to nonphotic cues such as locomotion activity and feeding
times, among others (Mrosovsky 1995, Harrington 1997, Verwey & Amir 2009, Morin 2013).
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a
M1
(Brn3b–)
M3
M2
M4
M5
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SCN
dLGN
IGL
vLGN
Nonphotic entrainment
Photoentrainment
b
Non-M1 ipRGCs
(OPN core–projecting)
M1 (Brn3b+) ipRGCs
(OPN shell–projecting)
M2
M3
Pupillary light reflex
M5
M4
OPN
Edinger–
Westphal
nucleus
Ciliary
ganglion
Figure 7
Reflexive behaviors controlled by intrinsically photosensitive retinal ganglion cells (ipRGCs). (a) Circadian
entrainment is intimately linked to ipRGCs. Direct retinal input to the suprachiasmatic nucleus (SCN) from
melanopsin-expressing (M1 and M2) ipRGCs is required for photoentrainment of circadian rhythms to a
24-hour light–dark cycle. Signals to the SCN via the ventral lateral geniculate nucleus (vLGN) and
intergeniculate nucleus (IGL), thalamic structures that receive direct inputs from a mix of ipRGC subtypes
(M1–M5), are involved in mediating nonphotic entrainment of circadian rhythms (e.g., entrainment to the
timing of wheel running episodes). (b) The olivary pretectal nucleus (OPN) is an obligatory structure in the
pupillary light reflex (PLR) pathway (ipRGCs → OPN → Edinger–Westphal nucleus → ciliary ganglion →
intrinsic eye muscles → pupil constriction). M1 ipRGCs expressing the transcription factor Brn3b project to
the shell of the OPN and have been genetically demonstrated to be essential for driving the PLR. The
contributions of other ipRGC subtypes that innervate the OPN core to the PLR remain to be determined.
Abbreviation: dLGN, dorsal lateral geniculate nucleus.
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These indirect routes from the eye to the SCN reside in the thalamus. The major retinorecipient
targets of the visual thalamic complex comprise three nuclei: the dLGN, the ventral LGN
(vLGN), and the intergeniculate leaflet (IGL). Although the dLGN has received the most experimental attention (see the subsection titled “The Dorsal Lateral Geniculate Nucleus: The Primary
Subcortical Target Feeding Visual Cortex”), it is noteworthy that in the mouse, the vLGN is
comparable in size to the dLGN, and it also receives inputs from multiple functionally distinct
RGC subtypes, including ipRGCs (Figures 6 and 7a). The IGL, also a fairly large target spanning
millimeters of the thalamus, receives retinal inputs from ipRGCs (Hattar et al. 2006, Ecker et al.
2010) (Figures 6 and 7a). A subset of neurons in the IGL/vLGN send direct projections to the
SCN, forming the geniculohypothalamic tract (Harrington 1997, Morin 2013) (Figure 7a).
What is the function of IGL and vLGN inputs to the SCN? Brief periods of light exposure
set at different times of day (a skeleton photoperiod) are sufficient for photoentrainment of the
circadian cycle in nocturnal animals. Edelstein & Amir (1999) demonstrated that rats in which the
IGL/vLGN was ablated failed to entrain to a skeleton photoperiod. The geniculohypothalamic
tract also appears to be part of the neural circuit that allows nonvisual cues to associate with
light–dark cycles in the natural environment. For example, Amir & Stewart (1996) demonstrated
that circadian rhythms could be reset by a nonphotic stimulus (20-minute exposure to air puffs)
in rats that had been trained to associate the air stimulus with a light cue that would normally
reset their circadian clocks. They implicated the IGL/vLGN as a potential location for integration
of this conditioned photic and nonphotic cue pairing on the basis of upregulation of the neural
activity marker Fos in these thalamic targets. More recently, Lumsden and colleagues (Delogu
et al. 2012) showed that disrupting the organization of postsynaptic cells in the IGL and vLGN
during development results in abnormal circadian entrainment by photic and nonphotic cues.
Interestingly, these mice also exhibited impaired negative masking (i.e., motor activity suppression
by light exposure during the active dark phase of a nocturnal animal). Therefore the IGL and
vLGN may represent part of a larger sensory integration network linking visual and nonvisual
pathways for regulating endogenous biological rhythms and behaviors.
The Pupillary Light Reflex: A Circuit for Ensuring that Appropriate
Light Levels Reach the Brain
The ambient luminance can vary drastically as we move from one location, such as a dimly lit room,
to another, such as a sunny street. Adjusting the aperture of the pupil, and thereby adjusting the
amount of light entering the eye, aids in optimizing image quality over such a wide range of light
levels (Campbell & Gregory 1960). The visual system is equipped with customized machinery,
built in large part from specific RGCs and their connections with the brain, that can change
the aperture of the pupil in response to changes in luminance levels. This adjustment involves
the subconscious, reflexive response of constricting or dilating the pupil, termed the pupillary
light reflex (PLR). Initially, only conventional rod- and cone-dependent pathways were thought
to drive the PLR. In 2001, however, this reflex was shown to persist in rodless, coneless mice
(Lucas et al. 2001), and, subsequently, the role of melanopsin signaling in driving the PLR was
revealed (Panda et al. 2003, Lucas et al. 2003). We now recognize that although rods and cones do
contribute to the PLR, direct activation of ipRGCs also plays a major role in this behavior. Indeed,
as we discuss below, the availability of molecular tools for marking and manipulating ipRGCs has
led to significant advances in understanding their contributions to this important reflex.
The olivary pretectal nucleus (OPN)—a midbrain retinorecipient target—is a critical hub
in the central PLR pathway (Trejo & Cicerone 1984; Young & Lund 1994; Gamlin & Clarke
1995; Gamlin et al. 1995; Distler & Hoffmann 1989a,b; Gamlin 2006). For many decades, it
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was unclear which RGCs specifically drive the PLR, largely because the location of the OPN—
nested between the dLGN and the SC and surrounded by many other small pretectal nuclei—
made selectively targeting the OPN for retrograde tracer injections difficult. Genetic tagging
of melanopsin-expressing RGCs with markers that also labeled their axons (Hattar et al. 2002,
2006; Ecker et al. 2010) revealed the crucial link between ipRGCs and the OPN and set the stage
for determining the role played by these neurons as the sole driver of the PLR. The peripheral
region, or shell, of the OPN receives inputs mainly from M1 ipRGCs, whereas the core region is
innervated by other ipRGCs, such as M2s (Hattar et al. 2002, 2006; Baver et al. 2008; Ecker et al.
2010) (Figure 7b). Targeted removal of ipRGCs as a general group, which can be accomplished
by toxin-based killing of these cells (Göz et al. 2008, Güler et al. 2008, Hatori et al. 2008) or
by altering the expression of transcription factors that drive their development (Mao et al. 2014,
Sweeney et al. 2014), abolishes the PLR. Deleting the transcription factor Eomes (also called
Tbr2) leads to the loss of all ipRGCs, resulting in a loss of retinal projections to the OPN and a
concomitant loss of light-induced pupillary constriction (Mao et al. 2014, Sweeney et al. 2014).
What role do the different OPN-projecting ipRGC subtypes play in driving the PLR? As
mentioned above, M1 ipRGC terminations are restricted to the OPN shell. Interestingly, neurons
in the OPN shell are strongly activated by changes in light levels and project to the Edinger–
Westphal nucleus (Prichard et al. 2002, Baver et al. 2008). Neurons in the Edinger–Westphal
nucleus in turn send projections to the ciliary ganglion, which activates the intrinsic eye muscles,
eventually leading to pupil constriction (Figure 7b). In a critical test of which RGCs drive the
PLR, Hattar and colleagues (Chen et al. 2011) showed that the M1 ipRGCs that feed the shell
of the OPN preferentially express the transcription factor Brn3b. They specifically killed these
OPN inputs by crossing mice that conditionally express a toxin from the Brn3b locus with mice
that express Cre recombinase from the melanopsin locus. This intersectional genetic ablation of
OPN-projecting M1 ipRGCs resulted in an abrogated PLR (Chen et al. 2011). The contributions
to the PLR of other non-M1 ipRGCs remain to be determined, as do those of the OPN core.
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A Reflex-Generating Circuit for Stabilizing Visual Scenes on the Retina
Head and eye movements cause images to slip on the retina. Left unchecked, such retinal slip would
lead to blurry perceptual representations of visual scenes. Thus, the eyes and brain generate compensatory head and eye movements in order to null this movement-generated effect. These gazestabilizing mechanisms are highly conserved and are found in all jawed species including frogs,
fish, birds, and mammals (reviewed in Masseck & Hoffmann 2009, Distler & Hoffmann 2011).
Two oculomotor reflexes, the optokinetic reflex (OKR) and the vestibuloocular reflex (VOR),
collaborate to generate the slip-compensating eye and head movements necessary for gaze stabilization. The VOR can drive near perfect image-stabilizing eye movements in response to fast head
movements because the biomechanics of the semicircular canals are ideal for detecting rapid head
rotations. When the head moves at velocities too slow to engage the vestibular system, however,
the OKR compensates for retinal slip. Thus, the visual and vestibular systems work in concert
over a wide range of movement velocities to elicit the compensatory eye movements necessary to
support accurate encoding of the visual scene (reviewed in Simpson 1984).
What are the visual circuit components that drive the optokinetic reflex? The AOS, a group of
functionally specialized RGCs and the brainstem targets to which they connect, has been implicated in generating the reflexive eye movements required for gaze stabilization in multiple species,
including in mice (Yonehara et al. 2008, 2009; Dhande et al. 2013; reviewed in Simpson 1984,
Giolli et al. 2006, Distler & Hoffmann 2011). Work done mainly in rabbits implicated On DSGCs
as the main RGC subtype involved in generating gaze-stabilizing eye movements (Simpson
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1984). These cells respond best to slow-moving visual stimuli, and their directional preferences
closely match the axes along which retinal slip is generated during head rotations. In addition,
retrograde labeling and electrophysiological recordings from the different terminal nuclei of the
AOS, namely, the nucleus of the optic tract (NOT) and the dorsal, medial, and lateral terminal
nuclei (DTN, MTN, and LTN, respectively), suggested that On DSGCs do indeed constitute
the primary RGC inputs (Simpson 1984, Distler & Hoffmann 2011). Thus, a model emerged
that the On DSGCs that encode forward motion and feed the NOT and DTN drive horizontal
reflexive eye movements, and those that encode upward and downward motion and innervate the
MTN drive vertical eye movements (Simpson 1984, Distler & Hoffmann 2011) (Figure 8).
a
On–Off DSGC
(forward)
On DSGC
(forward)
NOT–DTN
On DSGC
(downward)
On DSGC
(upward)
MTNd
MTNv
b
20
Angle (º)
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0
–20
0
5
10
15
Time (s)
Figure 8
The accessory optic system (AOS) generates reflexive eye movements necessary for stable visual perception. (a) Schematic showing
genetically identified direction-selective retinal ganglion cells (DSGCs) that innervate brainstem nuclei of the AOS, namely the nucleus
of the optic tract (NOT), and the dorsal and medial terminal nuclei (DTN and MTN, respectively). The NOT–DTN receives synaptic
inputs from both On and On–Off DSGCs encoding forward motion and drives horizontal eye movements. The dorsal and ventral
MTN (MTNd and MTNv, respectively) receive inputs from On DSGCs encoding upward and downward motion, respectively, and
are involved in generating vertical reflexive eye movements. For a detailed schematic of the fiber tracts that form the AOS, the reader is
referred to Pak et al. (1987) and Dhande et al. (2013). (b) Gaze-stabilizing eye movements can be quantitatively measured in head-fixed,
awake mice. Sample image (left) captured from a CCD camera used to record optokinetic nystagmus in response to horizontal or
vertical moving grating stimuli (middle) and example eye movement trace (right). Figure adapted with permission from Dhande et al.
(2013).
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Recent advances in viral, genetic, and behavioral tools reveal several striking similarities between the mouse and rabbit AOS. These advances are uncovering new facets of the functional
organization of the mouse accessory optic system. Studies using mice with genetically marked
RGCs have confirmed that On DSGCs also constitute the major retinal inputs to the mouse AOS
(Yonehara et al. 2008, Dhande et al. 2013). Moreover, Yonehara and colleagues (2009) showed
that neurons in the dorsal MTN of the mouse are strongly activated by upward motion and that
neurons in the ventral MTN respond preferentially to downward-moving visual stimuli. This
finding suggests that within the MTN, inputs from On DSGCs encoding upward motion are
compartmentalized separately from those encoding downward motion (Figure 8).
Two new studies (Kay et al. 2011, Dhande et al. 2013) discovered that the AOS also receives
an appreciable number of inputs from On–Off DSGCs. The MTN receives inputs from On–Off
DSGCs encoding downward motion, consistent with the role the MTN plays in driving vertical
compensatory eye movements (Kay et al. 2011). In contrast, the NOT receives inputs from a
population of On–Off DSGCs that are tuned to forward motion and prefer relatively slow-moving
visual stimuli, making these neurons ideal for participating in the role played by the NOT in
generating horizontal eye movements (Dhande et al. 2013) (Figure 8). Curiously, On–Off DSGCs
that prefer downward motion may also innervate the NOT (Kay et al. 2011). Collectively, analyses
of retinal projections to various AOS nuclei have begun to reveal the unexpected complexity of
the inputs received by these nuclei and presumably used to drive reflexive eye movements.
To date, researchers have not clearly established a causal relationship among specific AOSprojecting RGC types and image stabilization behavior. One study eliminated DS tuning of all
direction-selective retinal signals by killing the presynaptic inputs to DSGCs, namely, the cholinergic starburst amacrine cells. Interestingly, killing the starburst amacrine cells also eliminated
image-stabilizing eye movements (Yoshida et al. 2001). Silencing the On bipolar pathway has a
similar effect on this set of behaviors (Sugita et al. 2013). Together, these findings bolster the
correlation between On DSGCs and image-stabilizing eye movements. Both manipulations are
somewhat indirect, however, because they also alter retinal signals to the SC and the cortex,
both of which may provide feedback signals to the AOS network (reviewed in Gamlin 2006).
Thus, the contribution of specific RGC subtypes or AOS target nuclei in generating reflexive,
slip-compensating eye movements still remains unresolved.
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Integration of Diverse Visual Features in the Midbrain
The SC, homologous to the optic tectum in birds and cold-blooded vertebrates, is a large, multisensory structure in the midbrain and has a topographic map of visual space in its superficial layers.
Other sensory maps, such as somatosensory and auditory maps, reside below the visual input layers.
The SC harbors circuitry that is crucial for both convergence of multiple optic pathways and for
integration of afferent signals from multiple sensory modalities (reviewed in May 2006, Dhande
& Huberman 2014a). A key organizational feature of the SC is that all of its sensory maps are
integrated along the depth of the SC layers such that a sound occurring in a particular location
in visual space will generate a head or eye movement to that location (reviewed in Knudsen et al.
1987, Sparks & Nelson 1987, Stein et al. 2014). The SC is also a key structure for initiating motor
commands for head, body, and/or voluntary eye movements (e.g., saccades) with the goal of gaze
orientation (reviewed in Sparks 2002, Gandhi & Katnani 2011). In addition, considerable work in
primates has focused on the role of the SC in smooth pursuit and attention (reviewed in Wurtz
& Albano 1980, Mysore & Knudsen 2011, Krauzlis et al. 2013). The SC (optic tectum) has also
long been implicated in mediating innate defensive behaviors such as looming-evoked escape and
freezing in multiple species (Ingle 1973, Sahibzada et al. 1986, Dean et al. 1988, Ellard & Goodale
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300
Distance to nest (pixels)
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2°
20°
200
100
Flight
0
Nest
0
0.2
0.4
0.6
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Figure 9
An overhead threat elicits an innate flight response in mice. (a) Schematic showing how an expanding looming stimulus triggers an
escape flight to the home nest in mice. (b) Example trace [adapted from Yilmaz & Meister (2013)] showing the flight trace (red ) to the
nest ( green) evoked by a disc stimulus (black circles) expanding from 2 to 20◦ in diameter. The superior colliculus is thought to mediate
this innate aversive behavior.
1988, Northmore et al. 1988, Westby et al. 1990; reviewed in Dean et al. 1989). As a result of the
recent advances in genetic tools to visualize and manipulate neural circuits, along with the design
of more sophisticated behavioral analyses, the dissection of SC-mediated behaviors is once again
gaining attention (Yilmaz & Meister 2013, Liang et al. 2015, Shang et al. 2015, Wei et al. 2015)
(Figure 9).
The SC is one of the most distal retinorecipient targets. It is located on the dorsal surface
of the midbrain and is partially covered by the visual cortex. RGCs from the contralateral eye
terminate in the superficial layers of the SC to generate a continuous map of visual space. Axons
from the ipsilateral eye terminate in the slightly deeper layers of the SC and arise from RGCs
residing in the ventral retina, thereby establishing eye-specific visuotopic maps (reviewed in Assali
et al. 2014). For many decades, researchers have taken advantage of the clear structure–function
attributes of the SC, such as the precise organization of retinocollicular afferents, in order to
gain insight into the molecular and activity-dependent mechanisms that sculpt topographic maps
during development (reviewed in McLaughlin & O’leary 2005, Cang & Feldheim 2013, Kirkby
et al. 2013, Ackman & Crair 2014, Triplett 2014). Recently, the advent of transgenic tools for
labeling specific RGC subtypes and in vivo imaging techniques have allowed researchers to make
significant gains in understanding retinocollicular circuit organization and function in the mouse.
It is estimated that more than 90% of RGCs project to the SC in rodents (Linden & Perry 1983,
Hofbauer & Dräger 1985). This massive input raises some key questions, such as the following: Are
all the visual features relayed by the RGCs maintained as parallel maps within the SC? It has long
been known that different RGCs terminate at different depths in the SC of the cat and that of the
hamster (Bowling & Michael 1980, Berson 1988, Mooney & Rhoades 1990, Tamamaki et al. 1995),
and retrograde labeling experiments hinted that a similar overall organization might also exist in the
mouse (Hofbauer & Dräger 1985). Genetic labeling of RGCs that encode distinct visual features
(for example, different axes of motion, local object motion, or luminance) has now provided detailed
information on this issue by confirming that different RGC subtypes target distinct laminar depths
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in the retinorecipient SC, subdividing the SC into multiple distinct sublaminar maps (reviewed
in Dhande & Huberman 2014a). RGC axons that respond to local object motion selectively
terminate in the upper half of the retinorecipient SC (Kim et al. 2010). On–Off and Off DSGCs
also terminate in this domain of the SC (Kim et al. 2008, 2010; Huberman et al. 2009; Kay et al.
2011; Rivlin-Etzion et al. 2011). In contrast, ipRGCs and alpha RGCs terminate preferentially
in the lower half of the retinorecipient SC (Hattar et al. 2006, Huberman et al. 2008b, Brown
et al. 2010, Ecker et al. 2010). Clearly there are at least four distinct feature maps based on the
organization of retinal inputs, raising the issue of whether these maps are inherited and maintained
as distinct channels by collicular networks or are combined to generate more sophisticated feature
maps (see also Dhande & Huberman 2014a).
Several studies have documented the presence of a morphologically diverse collection of
excitatory and inhibitory neurons in the SC of rats and of mice (Langer & Lund 1974, May 2006,
Isa & Hall 2009, Gale & Murphy 2014). The four types of collicular neurons that have received
the most focus are (a) wide-field neurons, which have large dendritic trees that extend at oblique
angles to the SC surface; (b) horizontal cells, which are putative inhibitory projection neurons;
(c) narrow-field cells, which have vertical, cylindrically organized dendrites; and (d ) stellate
neurons, which have small, nonoriented dendritic fields (reviewed in May 2006). Gale & Murphy
(2014) recently demonstrated that these four classes of collicular neurons can be separated not
only based on morphometric parameters, but also on the basis of their visual response properties
and distinct projection patterns to downstream targets. So the question now becomes: Do
different SC neurons receive inputs from distinct RGC subtypes? Although the answer to this
question has yet to be resolved conclusively, recent studies have made significant progress in
extending our knowledge of the functional properties of different subtypes of collicular neurons
(Gale & Murphy 2014), and, from that, inferences can now be made about the preservation
and/or transformation of retinal signals arriving within the SC.
Ursula Dräger and colleagues (Dräger 1975; Dräger & Hubel 1975a, 1975b, 1976) were
pioneers in describing the functional cartography of sensory modalities in the mouse SC. Only
within the past few years, however, have researchers again begun to systematically probe the
visual properties of collicular neurons, this time using newly developed in vivo imaging and
electrophysiological tools in conjunction with genetic and classical anatomical tracing methods.
From this effort, it is becoming clear that neurons in the retinorecipient layers of the SC in mice
display a wide range of visual response properties, such as direction-independent motion selectivity; looming sensitivity; orientation and direction selectivity; and tuning for contrast, speed,
and size (Wang et al. 2009, 2010; Feinberg & Meister 2014; Gale & Murphy 2014; Ahmadlou &
Heimel 2015). Key issues to resolve in the coming years will be (a) the precise architecture of the
local SC circuits by which these properties arise (e.g., the integration of specific retinal afferents
onto specific SC target cells) and (b) how each component of these circuits contributes to specific
behaviors such as looming-evoked escape. We predict that genetic drivers for specific RGC and
SC neuron types will play a central role in assisting efforts to resolve these issues.
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What is the circuit diagram for generating feature selectivity in the superior colliculus?
Neurons in layer 5 of the visual cortex send topographically organized, direct glutamatergic inputs
to the SC. The mouse visual cortex also encodes visual features similar to those encoded in the SC,
such as looming, direction, and orientation, among others. (Wang et al. 2010, Feinberg & Meister
2014, Zhao et al. 2014a, Ahmadlou & Heimel 2015). Ablation or silencing of corticocollicular
inputs showed that this pathway modulates certain receptive field and tuning properties of SC
neurons, such as their gain, but these manipulations of the corticocollicular pathway do not appear
to result in a complete loss of feature selectivity in SC neurons (Wang et al. 2010, Feinberg &
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Meister 2014, Zhao et al. 2014a, Ahmadlou & Heimel 2015). These results contrast with those in
species such as cats and hamsters, in which certain response features of collicular neurons, such
as direction selectivity, critically depend on cortical inputs to the SC (Wickelgren & Sterling
1969, Rosenquist & Palmer 1971, Chalupa & Rhoades 1977). Although current studies suggest
that the visual features encoded by SC neurons in mice arise largely from cortex-independent
mechanisms, more explicit experiments to test whether or not cortical inputs can function as drivers
that impart feature selectivity to collicular neurons in mice remain to be performed. Another
possibility is that properties such as orientation and direction selectivity arise from local and
interlaminar circuits in the SC, as is the case for some tectal neurons in zebrafish (reviewed in
Gebhardt et al. 2013, Dhande & Huberman 2014a). Finally, the parsimonious explanation for
the diversity of visual features encoded by collicular neurons is that these properties are directly
inherited from the many different types of RGCs that project to the SC. As we discussed above,
different types of RGCs are capable of encoding these specialized visual features, and several of
these RGC subtypes (direction-selective, speed-tuned, motion-selective) send projections to the
SC. Recent advances in combining synaptic tracing techniques with in vivo two-photon imaging
(Rancz et al. 2011, Yonehara et al. 2013, Wertz et al. 2015) have made this hypothesis readily
testable.
The Dorsal Lateral Geniculate Nucleus: The Primary Subcortical
Target Feeding Visual Cortex
The dLGN is a prominent thalamic relay station that, together with the SC, forms the majority of
the optic pathway in all mammals. The dLGN has received much attention from vision researchers
working in a variety of species because it is the gateway for visual information to reach the primary
visual cortex (V1) and thus the starting point of higher-order visual representations and perception
(see Usrey & Alitto 2015). The dLGN receives inputs from several different RGC subtypes, and all
of these inputs appear to be organized in a retinotopic manner (Pfeiffenberger et al. 2006, Piscopo
et al. 2013). In addition, binocular inputs are segregated on the basis of the eye of origin within
the dLGN (Rakic 1976, Jaubert-Miazza et al. 2005), a feature that has served as a valuable model
for understanding how both spontaneous activity during development and visual experience shape
neural circuits in a wide variety of species (reviewed in Katz & Shatz 1996, Huberman et al. 2008a,
Hong & Chen 2011, Kirkby et al. 2013).
The image of the six-layered primate dLGN is iconic in neuroscience. Rodents, however, lack
overt cytoarchitectural lamination in their dLGN (Reese 1988). The recent discovery of genetic
tools that label the axons of functionally defined RGC subtypes confirmed that RGCs projecting
to the mouse dLGN are organized into discrete visual channels that have a laminar-specific overall
organization (Kim et al. 2008, 2010; Huberman et al. 2008b, 2009; Ecker et al. 2010; Kay et al.
2011; Rivlin-Etzion et al. 2011; reviewed in Dhande & Huberman, 2014a). Broadly speaking,
the mouse dLGN can be divided into a core region and a shell region, each of which contains
a complete retinotopic map and receives input from functionally distinct populations of RGCs
(Figure 10). The core compartment of the dLGN receives input from alpha-like RGCs, which
are not directionally tuned and which respond best to center–surround stimuli (Huberman et al.
2008b), as well as from ipRGCs (Brown et al. 2010, Ecker et al. 2010). In contrast, the shell of the
dLGN, receives input from multiple direction-selective RGC subtypes, each of which is tuned to
one of the four cardinal directions (Kim et al. 2008, 2010; Huberman et al. 2009; Kay et al. 2011;
Rivlin-Etzion et al. 2011).
In recent studies of the postsynaptic organization of dLGN neurons, Guido and colleagues
(Krahe et al. 2011, Seabrook et al. 2013, Bickford et al. 2015) demonstrated that the mouse dLGN
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contains at least four distinct types of neurons. Morphologically, these neurons resemble the X, Y,
and W relay neurons described in cats and primates, as well as the local inhibitory neurons. Interestingly, some dLGN neuron subtypes exhibit shell versus core distribution biases. For example,
W-like cells are found predominantly in the shell (where DSGC inputs terminate), whereas Y-like
cells reside in the core. X-like cells and local inhibitory neurons are found dispersed across both
the shell and the core. Neurons in the mouse dLGN respond to a variety of visual features, such as
direction of motion, stimulus orientation, and contrast modulation (Marshel et al. 2012, Piscopo
et al. 2013), but how the visual properties of X, Y, W, and inhibitory cells are determined by
their specific RGC inputs remains unclear (Figure 10). RGC receptive field properties no doubt
influence the shape of dLGN cell responses, but the convergence of RGC projections (1–6 RGCs
synapse onto each dLGN neuron) (Chen & Regehr 2000, Jaubert-Miazza et al. 2005, AcunaGoycolea et al. 2008, Seabrook et al. 2013) offers ample opportunity for both combining different
visual feature properties (e.g., opposing directionally tuned inputs) onto individual dLGN neurons
and, by virtue of connections with inhibitory dLGN cells, possibly sharpening those features (see,
e.g., Levick et al. 1969).
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Challenging the textbook model of cortical orientation and direction selectivity? Since
the discovery of orientation-selective neurons in the cat and primate visual cortex by Hubel &
Wiesel in the 1960s (Hubel & Wiesel 1962, 1968), along with the subsequent discovery of orientation columns and pinwheel organization in the cortex (Blasdel & Salama 1986, Ts’o et al. 1990,
Bonhoeffer & Grinvald 1991), orientation selectivity has been a major focus of research in visual
neuroscience. Traditionally, studies addressing the emergence and underlying mechanisms of orientation selectively have used rabbits, cats, ferrets, or primates as their model of choice. However,
all of the recent advances in genetic and optical imaging tools for recording neural activity in
rodents have made mouse V1 a premier model system for studies exploring the organization and
mechanistic basis of orientation selectivity. Mouse V1 is rich with neurons that respond strongly
to bars of light of a particular orientation (Dräger 1975, Mangini & Pearlman 1980, Métin et al.
1988, Niell & Stryker 2008), but unlike the columnar and pinwheel organization of orientationselective cortical neurons described in higher mammals, orientation-selective neurons in mouse
(and rat) V1 are organized in a salt-and-pepper fashion (Girman et al. 1999, Ohki et al. 2005,
Sohya et al. 2007, Kerlin et al. 2010, Bonin et al. 2011).
The long-standing model for cortical orientation selectivity is that untuned center–surround
inputs from thalamic relay cells are summed by each cortical neuron to generate elongated cortical
receptive fields that are strongly activated by oriented bars of light. Although this method may be
the dominant one for generating orientation-selective units in the cortex, recent studies confirm
that orientation-selective (axial) and direction-selective neurons are certainly present in the dLGN
of both rodents and primates. Marshel et al. (2012) studied populations of neurons residing in
the dorsal tip of the dLGN and, using in vivo two-photon calcium imaging, identified dLGN
neurons that were tuned for motion in the anterior and posterior directions, as well as neurons
that were tuned to axial motion along the horizontal (anterior–posterior) axis. Subsequent studies
used recording electrodes that allow for a more thorough investigation of the entire dLGN to
establish the existence of mouse dLGN neurons that encode directional and axial motion along
both the vertical (upward–downward) and horizontal axes (Piscopo et al. 2013, Scholl et al. 2013,
Zhao et al. 2013). Recently Cruz-Martı́n and colleagues (2014) discovered that direction- and
orientation-tuned inputs from the dLGN do indeed feed superficial layers 1 and 2 of the mouse
visual cortex (Figure 10). Furthermore, Cheong et al. (2013) found that in marmosets, a small
portion of neurons in the koniocellular layers of the dLGN are strongly orientation selective (on
par with V1 units), and, recently, Ling et al. (2015) discovered orientation-selective responses in
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Cortical neuron properties
V1
1
2
Directionselective
Orientationselective
Loomingselective
Suppressedby-contrast
• Métin et al. 1988
• Niell &
Stryker 2008
• Dräger 1975
• Mangini &
Pearlman 1980
• Métin et al. 1988
• Niell &
Stryker 2008
• Zhao et al. 2014
• Niell &
Stryker 2010
3
4
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5
6
dLGN relay neuron properties
~15,000 axons
dLGN
Directionselective
Orientationselective
Non-directionselective
~50,000 axons
Ganglion cell properties
Retina
Direction-selective
On–Off DSGCs
Non-direction-selective
t-Off alpha
Figure 10
Visual features encoded by the retinogeniculocortical pathway in mice. Cells at each level of this pathway have been shown to possess a
variety of functional properties, some of which overlap. For example, direction-selective ganglion cells (DSGCs) send signals to cells in
the dorsal lateral geniculate nucleus (dLGN) that, in turn, exhibit direction-selective signals in their axon terminals in the visual cortex
(V1). In contrast, other properties (e.g., looming selectivity) have so far only been described in cells in V1. It is not yet clear whether
these cells inherit these properties from the geniculocortical projecting cells that innervate them or from complex local circuits within
V1. Given the high level of convergence within the retinogeniculocortical pathway, however, properties unique to V1 neurons likely
arise from a combination of the properties of the geniculocortical projecting neurons that innervate them, as well as from modification
resulting from local circuits within the cortex.
the human LGN using fMRI. Therefore, in light of the growing body of work demonstrating
the presence of orientation-selective responses in the LGN, the textbook framework for cortical
orientation selectivity may need revision.
What is the source of orientation selectivity in the dLGN? One possibility is that corticogeniculate projections impose this feature on relay neurons. This possibility is unlikely, however, because
cortical inactivation does not impair the orientation selectivity of geniculate neurons (Scholl et al.
2013, Zhao et al. 2013). Another possibility is that combining DSGC inputs with opposing direction preferences generates orientation-selective units in the dLGN. During their survey of the
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visual response properties of dLGN neurons, Piscopo et al. (2013) found an enriched presence of
direction- and orientation-selective units in the dorsolateral shell of the LGN, the same region
in which different subtypes of DSGCs are known to terminate (Huberman et al. 2009, Kim et al.
2010, Rivlin-Etzion et al. 2011). In addition, Cruz-Martı́n et al. (2014) used transsynaptic rabies
tracing techniques to show that relay neurons in the shell do in fact receive synaptic connections
from DSGC axon terminals (Figure 10). Collectively, this work suggests that direction-selective
responses in the dLGN are a direct consequence of their presynaptic partners, DSGCs. A third
possibility is that orientation selectivity in the dLGN arises from inputs from the SC onto the
dLGN. Several collicular neurons are orientation selective (see below), and, in rodents, collicular
projections are known to terminate exclusively in the shell region of the dLGN (Reese 1984,
Harting et al. 1991, Grubb & Thompson 2004, Bickford et al. 2015).
Finally, orientation-selective cells in the thalamus could also arise as a direct consequence of
orientation selectivity in the retina. Orientation-selective ganglion cells are found in rabbits and
cats, and they are likely present in primates as well (Levick & Thibos 1982, Bloomfield 1994,
Passaglia et al. 2002). Zhao et al. (2014a) used retinal multielectrode array recordings to show
that orientation-selective cells are also present in the mouse retina. However, targeted recordings
of orientation-selective ganglion cells have not yet been performed, nor have their dendritic
morphology and projections to central visual targets been revealed.
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Isoorientation domains in the superior colliculus: a bit of primate V1 in the mouse superior
colliculus. A significant fraction of neurons in the superficial retinorecipient layers of the SC
display strong responses to oriented visual stimuli (Wang et al. 2009, 2010; Feinberg & Meister
2014; Gale & Murphy 2014). Feinberg & Meister (2014) recently developed a surgical preparation
that allows for visualization of the SC without compromising the overlying cortex. By exposing
the SC in this manner, they were able to map the population activity of collicular neurons using
genetically encoded calcium sensors (GCaMP6) in awake, head-fixed mice. In doing so, they
made a remarkable discovery: Orientation-selective neurons in the mouse SC have a columnar
organization similar to that in cats and primates. Ahmadlou & Heimel (2015) used a combination of
extracellular electrode recordings and in vivo calcium imaging techniques, and they not only came
to the same conclusion but also found that these orientation maps are organized in a concentric
manner. Interestingly, both their study and the one by Feinberg & Meister found that not all
orientations are represented at every retinotopic location. Collicular orientation selectivity in
mice appears to be independent of cortical inputs (Wang et al. 2010, Feinberg & Meister 2014,
Ahmadlou & Heimel 2015). Thus, orientation selectivity in the SC is likely to be generated by
combining inputs from direction-selective or orientation-selective RGC afferents and/or is an
emergent property of intracollicular circuits.
As mentioned above, the classic model for orientation selectivity posits that selectivity for
stimulus orientation in areas such as the visual cortex arises from the specific organization pattern of
non-orientation-tuned thalamic inputs. This model may not hold true for some species, however,
given that orientation selectivity is now reported at every stage of the retinogeniculocortical
pathway and that orientation columns have been discovered in the mouse SC. It will be exciting
to empirically test how the selectivity present in higher visual areas for stimulus features such as
orientation and direction relates to selectivity for the same features encoded by the retina.
ipRGCs: new players in the retinogeniculocortical pathway. An emerging theme in vision
science is the contribution of ipRGCs to pattern vision. The recent development of transgenic
mice with genetically tagged ipRGCs revealed that the axons of ipRGCs project to the core region
of the dLGN (Hattar et al. 2006, Brown et al. 2010, Ecker et al. 2010). Brown and colleagues
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(2010) assessed the visual response properties of dLGN and cortical neurons in wild-type mice
and in mice lacking either melanopsin or rod and/or cone photoreceptors. Surprisingly, they
found that melanopsin-mediated light responses can drive nearly 40% of LGN neurons and
can thereby also drive neurons in V1 (Brown et al. 2010). Moreover, in a more recent study,
they showed that melanopsin signaling can influence the spatial and temporal frequency tuning
of neurons in the LGN and can impact the speed preference of LGN neurons in response to
moving gratings (Allen et al. 2014).
How does the ipRGC pathway influence pattern vision and behavior? Hattar and colleagues
demonstrated that mice with altered cone and rod function but intact melanopsin signaling could
still discriminate high-contrast spatial patterns, although the altered animals had to perform more
perceptual trials than wild-type animals (Ecker et al. 2010). Studies have also shown that some On
alpha-like RGCs express melanopsin (M4 ipRGCs), are intrinsically photosensitive, and project to
the core compartment of the dLGN (Estevez et al. 2012) and, very recently, melanopsin signaling
and M4 ipRGCs in particular were implicated as being essential for normal contrast detection
(Schmidt et al. 2014).
We note that the presence of ipRGC input to the dLGN is not specific to rodents; ipRGCs have
also been shown to project to the dLGN in macaque monkeys (Dacey et al. 2005). Melanopsinexpressing RGCs are also present in the human retina (Hannibal et al. 2004, Dacey et al. 2005), but
their central projection patterns have not yet been charted. Nevertheless, these studies collectively
build a strong case that ipRGCs are not just part of primordial visual pathways that entrain circadian
rhythms and drive the PLR. Rather, they appear to play several critical roles in supporting more
traditionally studied aspects of vision, including visual perception itself.
DISCUSSION
In this article, we have attempted to convey the rich set of signals that RGCs encode, along with
the brain circuits that unpack and use that information. The field of visual neuroscience has clearly
entered a particularly exciting phase in which classic techniques such as dye labeling and single
unit electrophysiology are being joined by modern genetic tools for marking, monitoring, and
manipulating the activity of specific retinal output pathways. These tools in turn enable scientists
to define the precise contributions of these pathways to vision. Given that the past decade has
brought genetic identification of ∼75% of all mouse RGC subtypes, the next major effort will be
to apply the same logic to identification of specific types of visual neurons within the brain. One
can also imagine that in the next decade, there will be a significant expansion in the effort to bring
molecular tools to the study and control of retinal and brain neurons in nonhuman primates and
in humans. The motivation for this is to understand how we process visual information and to
improve quality of life by better diagnosing and treating diseases that compromise vision.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
We thank Drs. David Berson, Rana El-Danaf and Tania Seabrook for helpful comments on
earlier versions of this manuscript. A.D.H. is supported by the National Institutes of Health and
National Eye Institute (grants: RO1 EY022157 and U01 U01NS090562), the Glaucoma Research
Foundation Catalyst for a Cure Initiative, the E. Matilda Ziegler Foundation, the Pew Charitable
www.annualreviews.org • RGC Contributions to Feature Processing
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Trusts, and the McKnight Foundation. O.S.D was supported in part by the Knights Templar
Postdoctoral Fellowship and Grant.
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Annual Review of
Vision Science
Contents
Volume 1, 2015
An autobiographical article by Horace Barlow is available online at
www.annualreviews.org/r/horacebarlow.
Image Formation in the Living Human Eye
Pablo Artal p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1
Adaptive Optics Ophthalmoscopy
Austin Roorda and Jacque L. Duncan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p19
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What Does Genetics Tell Us About Age-Related Macular Degeneration?
Felix Grassmann, Thomas Ach, Caroline Brandl, Iris M. Heid,
and Bernhard H.F. Weber p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p73
Mitochondrial Genetics and Optic Neuropathy
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Zebrafish Models of Retinal Disease
Brian A. Link and Ross F. Collery p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 125
Angiogenesis and Eye Disease
Yoshihiko Usui, Peter D. Westenskow, Salome Murinello, Michael I. Dorrell,
Leah Scheppke, Felicitas Bucher, Susumu Sakimoto, Liliana P. Paris,
Edith Aguilar, and Martin Friedlander p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 155
Optogenetic Approaches to Restoring Vision
Zhuo-Hua Pan, Qi Lu, Anding Bi, Alexander M. Dizhoor, and Gary W. Abrams p p p p 185
The Determination of Rod and Cone Photoreceptor Fate
Constance L. Cepko p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 211
Ribbon Synapses and Visual Processing in the Retina
Leon Lagnado and Frank Schmitz p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 235
Functional Circuitry of the Retina
Jonathan B. Demb and Joshua H. Singer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 263
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Contributions of Retinal Ganglion Cells to Subcortical Visual Processing
and Behaviors
Onkar S. Dhande, Benjamin K. Stafford, Jung-Hwan A. Lim,
and Andrew D. Huberman p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 291
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Organization of the Central Visual Pathways Following Field Defects
Arising from Congenital, Inherited, and Acquired Eye Disease
Antony B. Morland p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 329
Visual Functions of the Thalamus
W. Martin Usrey and Henry J. Alitto p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 351
Neuronal Mechanisms of Visual Attention
John Maunsell p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 373
A Revised Neural Framework for Face Processing
Brad Duchaine and Galit Yovel p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 393
Deep Neural Networks: A New Framework for Modeling Biological
Vision and Brain Information Processing
Nikolaus Kriegeskorte p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 417
Visual Guidance of Smooth Pursuit Eye Movements
Stephen G. Lisberger p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 447
Visuomotor Functions in the Frontal Lobe
Jeffrey D. Schall p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 469
Control and Functions of Fixational Eye Movements
Michele Rucci and Martina Poletti p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 499
Color and the Cone Mosaic
David H. Brainard p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 519
Visual Adaptation
Michael A. Webster p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 547
Development of Three-Dimensional Perception in Human Infants
Anthony M. Norcia and Holly E. Gerhard p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 569
Errata
An online log of corrections to Annual Review of Vision Science articles may be found at
http://www.annualreviews.org/errata/vision
viii
Contents