Field, Sampath Rieke (2005)

Field, Sampath Rieke (2005)
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10.1146/annurev.physiol.67.031103.151256
Annu. Rev. Physiol. 2005. 67:491–514
doi: 10.1146/annurev.physiol.67.031103.151256
c 2005 by Annual Reviews. All rights reserved
Copyright !
First published online as a Review in Advance on October 12, 2004
RETINAL PROCESSING NEAR ABSOLUTE
THRESHOLD: From Behavior to Mechanism
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Greg D. Field, Alapakkam P. Sampath, and Fred Rieke
Department of Physiology and Biophysics, University of Washington, Seattle,
Washington 98195; email: [email protected]; [email protected];
[email protected]
Key Words visual sensitivity, photon detection, signal processing, physical limits,
scotopic vision
■ Abstract Vision at absolute threshold is based on signals produced in a tiny
fraction of the rod photoreceptors. This requires that the rods signal the absorption
of single photons, and that the resulting signals are transmitted across the retina and
encoded in the activity sent from the retina to the brain. Behavioral and ganglion
cell sensitivity has often been interpreted to indicate that these biophysical events
occur noiselessly, i.e., that vision reaches limits to sensitivity imposed by the division of light into discrete photons and occasional photon-like noise events generated
in the rod photoreceptors. We argue that this interpretation is not unique and provide a more conservative view of the constraints behavior and ganglion cell experiments impose on phototransduction and retinal processing. We summarize what is
known about how these constraints are met and identify some of the outstanding open
issues.
INTRODUCTION
Sensory signals are inherently variable. These variations set a fundamental limit
to the performance of any system designed to detect and process these signals.
In the case of vision, sensitivity cannot exceed the limit set by the quantization
of light into discrete photons and the consequent Poisson fluctuations in photon
absorption. Several aspects of dark-adapted visual processing approach this limit.
Rod photoreceptors reliably signal the absorption of single photons (1, 2), and behavioral detection may require absorption of only a few photons (3). This exquisite
sensitivity is crucial for normal night vision. On a moonless night only one rod in
10,000 receives a photon during the integration time of the rod signals [reviewed
in (4)]. Thus visually guided behavior requires the retina to read out and process
single-photon signals carried by a tiny fraction of the rods while rejecting noise
generated in the remaining rods.
Reaching the sensitivity limit imposed by the quantal nature of light requires
that intrinsic noise is small compared with that generated by Poisson fluctuations
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in photon absorption. Both behavioral (5–7) and physiological (2, 8, 9) studies
have identified conditions under which this requirement is not met; under these
conditions intrinsic noise limits sensitivity. The intrinsic noise has been associated
with photon-like noise events produced by spontaneous activation of rhodopsin in
the rod photoreceptors (5, 8, 9). If correct, this idea implies that the retinal readout
of single-photon responses in the rod array is efficient and effectively noiseless.
However, uncertainty in existing estimates of behavioral sensitivity and of rod
noise makes this identification tenuous. Several additional noise sources, including
other sources of rod noise, synaptic noise in retina and cortex, and noise in spike
generation, could also contribute to limiting behavioral sensitivity. Although it is
not clear whether these other noise sources are negligible, behavioral sensitivity
does require that they are small.
Behavioral work has motivated studies of how absorbed photons are transduced
by rods and how the resulting signals are processed by the retinal circuitry. This
work has focused on several questions: (a) How do rods detect single photons
(1, 2)? (b) How are the resulting signals reliably transmitted across retinal synapses
(10–12)? (c) How are the signals produced by absorption of a few photons coded
in the pattern of activity sent from the retina to the brain (8)? Similar problems
occur elsewhere in the nervous system. For example, signals in some cortical areas,
like those in the rod array, are sparsely coded (13, 14). In such cases, appropriate
strategies for computations based on the encoded signals must take the sparseness
into account. These general questions can be posed clearly in the retina because
the signal and noise properties of the rod inputs can be measured and the stimuli
can be precisely controlled.
Here we review what is known about how the mammalian retina works near
absolute visual threshold. We begin by discussing evidence from behavioral experiments and ganglion cell recordings that the visual system can encode the
absorption of a small number of photons. We then describe the properties of the
noise inferred from behavioral and ganglion cell experiments. A clear prediction
from behavioral and ganglion cell sensitivity is that rod photoreceptors can detect
single photons; we summarize the mechanisms responsible and highlight the rod
noise sources that could limit sensitivity. Finally we discuss the retinal circuitry
that conveys the rod signals to the ganglion cells, emphasizing how this circuitry
processes single-photon responses.
BEHAVIOR
Behavioral experiments provide estimates of the minimum number of photon absorptions that can be detected and the noise that limits the reliability of detection.
These experiments are often interpreted to indicate that humans can detect <10
absorbed photons (3, 6, 15), limited by the spontaneous activation of rhodopsin
in the rod photoreceptors (5). We argue that these interpretations are not unique.
Thus it is currently unclear what process or processes limit behavioral sensitivity.
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Consequently, although behavior constrains the physiological mechanisms responsible for dark-adapted visual sensitivity, the extent of these constraints is not clear.
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Frequency of Seeing Experiments
Not long after it was established that light is quantized into discrete photons,
Lorentz realized that a just-detectable flash delivered about 100 photons to the
cornea and hence that the quantization of light might be of relevance for visual
sensitivity [reviewed in (16)]. Identifying the minimum number of photons required
for seeing from this observation is difficult because of uncertainty in the quantum
efficiency, i.e., the fraction of photons at the cornea absorbed by the rods. Estimates
of the quantum efficiency based on measurements of the scatter and absorption
properties of structures in the eye (the absorptive quantum efficiency) range from
0.1 to 0.3 (17), indicating that 10–30 photons are required for seeing a flash.
The frequency of seeing experiments of Hecht and colleagues (3) and van der
Velden (15) estimated the detection threshold and quantum efficiency directly
from behavior. Measurement of the fraction of trials in which a flash was seen
as a function of the number of photons at the cornea showed a broad transition
from flashes that were rarely seen to those frequently seen (e.g., Figure 1A). The
threshold and quantum efficiency were estimated on the basis of three assumptions:
First, variability in a subject’s responses was attributed to the Poisson statistics of
photon absorption and the consequent trial-to-trial fluctuations in the number of
absorbed photons. Second, only flashes producing at least ! absorbed photons
were seen. Third, the average number of photons contributing to seeing, n̄, was
related to the average number of photons at the cornea, N̄ , by an unknown quantum
efficiency, n̄ = Q E N̄ . Thus the probability of seeing a flash delivering an average
of N̄ photons to the cornea is
Psee =
∞
!
exp(−Q E N̄ )(Q E N̄ )n
.
n!
n≥!
1.
This approach estimates the behavioral threshold, !, from the steepness of the
transition between flashes that are rarely and almost always seen. If ! is small,
Poisson fluctuations in the number of absorbed photons from one trial to the next
will make the transition broad. The transition becomes steeper with increasing !.
When the probability of seeing from Equation 1 is plotted against the logarithm
of the flash strength, the unknown quantum efficiency shifts the curve along the
flash strength axis but does not change its shape. Thus the quantum efficiency
is estimated from the shift required to align Equation 1 with the frequency of
seeing data (Figure 1A). From this analysis, Hecht and colleagues estimated a
threshold of 5–7 photons and a quantum efficiency of ∼0.06. This behavioral
quantum efficiency is considerably lower than estimates of the absorptive quantum
efficiency, a point we return to below. Even with the higher quantum efficiency,
the likelihood that an individual rod absorbed >1 photon on any trial is small
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Figure 1 Frequency of seeing data adapted, with permission, from Teich and colleagues (7). (A) Data for low false-positive rate fit with Equation 1. ! is the threshold
number of photons, and Q E is the quantum efficiency. (B) Frequency of seeing curves
for high (◦) and low (•; identical to A) false-positive rates from a single subject. Fits
from Equation 2 include additive Poisson noise, constrained to be equal for each curve,
and different thresholds. (C) Data in B replotted and fit with Equation 2 with a higher
quantum efficiency, higher thresholds, and increased additive Poisson noise. (D) Data
replotted and fit with Equation 3, which includes multiplicative Poisson noise. The
multiplicative noise allows for a higher quantum efficiency, a lower threshold, and less
additive Poisson noise.
because the flashes covered an area of the retina containing ∼500 rods. Thus these
experiments require that individual rod photoreceptors detect single photons.
False Positives and Internal Noise
The frequency of seeing analysis described above assumes that all the noise in the
visual system is the result of Poisson fluctuations in photon absorption. If true,
visual sensitivity would reach the limit imposed by the division of light into discrete photons. However, observers occasionally report seeing a flash even when
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PHOTON DETECTION IN THE RETINA
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none is delivered, and detection threshold depends on the rate of these false-positive
responses. Barlow demonstrated this dependence by allowing observers to adopt
two criteria, answering either yes or maybe in a frequency of seeing experiment;
the maybe responses had a lower threshold and higher false-positive rate than the
yes responses (5) (e.g., Figure 1B). Barlow used these results to argue that the
false positives were produced by internal noise that occasionally generated
the false perception of a flash. This noise, together with Poisson fluctuations in
photon absorption, was interpreted as limiting detection performance.
Sakitt (6) did a more complete version of Barlow’s yes-maybe experiment by
asking subjects to rate the strength of each of a series of dim flashes on a scale from
0 to 6, where 0 corresponds to “did not see anything” and 6 to “very bright light.”
She constructed frequency of seeing curves and estimated thresholds for ratings of
1 or more, 2 or more, 3 or more, etc. This amounts to measuring thresholds with six
different criteria. The false-positive rate and sensitivity decreased as the criterion
increased. The experiments of Sakitt and Barlow show that false positives can
trade for detection threshold across a wide range of criteria. In this view, different
criteria correspond to different signal-to-noise ratios, and observers can choose
where to operate on the basis of how many mistakes they are allowed to make.
These experiments indicate that a small number of photons, perhaps even a single
photon, contributes to detection.
Barlow and Sakitt converted the false-positive rates in their respective experiments into an estimate of the internal noise limiting performance. They assumed
this noise could be expressed as an additive dark light complete with Poisson
fluctuations. In this case, following Equation 1, the probability of seeing is
Psee =
∞
!
exp[−Q E ( N̄ + D)][Q E ( N̄ + D)]n
.
n!
n≥!
2.
Here D is the additive Poisson noise (the dark light), expressed as an equivalent
number of photons at the cornea. In darkness (i.e., N̄ = 0) the probability of
seeing is nonzero because of the dark light. Barlow fit the yes and maybe results
with Equation 2, assuming they shared a common amount of dark light but had
different thresholds (e.g., Figure 1B). Sakitt similarly fit her data such that only
the threshold changed between different criteria. Estimated values of the dark light
and difficulties in obtaining a unique estimate are discussed below.
Ambiguities in Behavioral Measurements
The central problem with interpretation of the behavioral experiments summarized above is that the fits to the frequency of seeing curves are not unique. We
have described the discrepancy between the behavioral and absorptive quantum
efficiencies: Behavioral measurements place the quantum efficiency between 0.03
and 0.06, whereas direct estimates based on losses within the eye range from
0.1 to 0.3. Barlow suggested that this discrepancy could originate because the
behavioral quantum efficiency can trade for additive Poisson noise when fitting
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frequency of seeing data with Equation 2 (5). This is illustrated in Figure 1C,
which shows fits to the frequency of seeing data from Teich and colleagues (7)
with a quantum efficiency of 0.11. Raising the quantum efficiency from 0.05 to
0.11 increased the threshold by a factor of ∼4.5 and the dark light by a factor of ∼3
(Figure 1B).
Uncertainty in the quantum efficiency produces a 10-fold range in estimates of
threshold and dark light. Expressed as an equivalent rate of photon-like noise events
in each rod photoreceptor (using estimates of the spatial and temporal summation
of the rod array, the rod density, and an assumed quantum efficiency), the dark light
ranges from 0.002 to 0.03 s−1 (18). This large range of values makes it impossible
to draw a strong association between the dark light and noise originating in the
rods.
Figures 1B, C illustrate two examples in a range of possible fits to the frequency
of seeing data. At one end of this continuum, the number of photons required for
detection is <10, the dark light is close to estimates of rod noise, but the behavioral
quantum efficiency is very low compared with the absorptive quantum efficiency. A
possible explanation is that at least half of the photons absorbed by the rods are not
processed. Although it sounds counterintuitive, this can be an effective processing
strategy (discussed below). At the other end of the continuum, the behavioral and
absorptive quantum efficiencies agree, but the threshold number of photons and the
amount of dark light are high. These extremes provide qualitatively different views
of retinal processing. In the first, the retina efficiently and noiselessly processes
the rod signals. In the second, the dark light cannot be explained by rod noise alone
and instead post-rod processing must be noisy or inefficient.
Another possible resolution of the discrepancy between behavioral and absorptive quantum efficiencies is that the additive noise model is wrong. Lillywhite (19)
showed that multiplicative noise could help resolve this discrepancy. Such noise
could arise if several Poisson noise sources operate sequentially, and hence the
product of their probability distributions determines the response statistics. Additive noise is still required to explain the false positives.With both additive and
multiplicative Poisson noise, the probability of seeing becomes
Psee =
∞
∞
!
exp[−Q E ( N̄ + D)][Q E ( N̄ + D)]n !
exp(−Mn)(Mn)s
.
n!
s!
s≥!
n≥0
3.
The term inside the first sum is the probability of n photon-like events given the
quantum efficiency Q E , the mean number of photons at the cornea N̄ , and the dark
light D. The second sum determines the probability that the response s is equal to
or greater than the threshold ! given n absorbed photons. M is the gain from n
to s, such as might be observed if one photon generated two spikes on average in
a ganglion cell. Figure 1D shows fits for the frequency of seeing data according
to Equation 3. A combination of additive and multiplicative noise allows for a
quantum efficiency of 0.2 and relatively low thresholds (7, 20).
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Summary
Behavioral measurements of dark-adapted sensitivity place several constraints on
visual processing: (a) Individual rods can detect single photons; (b) false positives
and sensitivity can trade for one another; and (c) signals from a small number of
photons, perhaps a single photon, influence perception. A key open issue is whether
the retina is efficiently processing the rod signals. Can the dark light be attributed
to noise events in the rods? Does the apparent discrepancy between behavioral and
absorptive quantum efficiencies result from the discarding of photon responses
and/or the presence of multiplicative noise? Alternatively, is the discrepancy a
result of incorrectly assuming both the threshold number of photons and the amount
of dark light are relatively small? The physiological work described below provides
some answers.
RETINAL GANGLION CELLS
The elegance and the curse of the behavioral experiments is that they lump together
all factors that could lower sensitivity, including many central factors (17, 21) that
are a nuisance if the aim is to constrain retinal processing. An alternative is to
focus on the fidelity of signals in the retinal ganglion cells. The most extensive of
such studies in mammals are in the anesthetized cat.
Barlow, Levick, and colleagues (8, 22, 23) recorded extracellular spikes from
individual ON ganglion cells probably ON-X according to (Reference 9) in darkness
and in response to dim light flashes. The spontaneous activity in darkness consisted
of bursts of several spikes. The distribution of intervals between bursts was approximately exponential, indicating that the bursts occur largely independently.
This behavior is consistent with the idea that the maintained discharge is produced
by additive Poisson noise consisting of independent, discrete noise events, each of
which produces a burst of ∼3 spikes.
Barlow and colleagues also analyzed responses to flashes delivering 5–50 photons at the cornea, using a frequency of seeing analysis similar to that used in
Sakitt’s behavioral measurements (8) and a receiver operator characteristic analysis (23). They concluded that the performance of dark-adapted ganglion cells
could be explained with the following assumptions: (a) The quantum efficiency
was 0.18, (b) each absorbed photon caused the ganglion cell to generate 2–3 extra
spikes, and (c) detection was limited by Poisson fluctuations in photon absorption
and additive Poisson noise arising from discrete noise events (dark light) occurring
at a rate of 5–6 s−1 .
Mastronarde provided additional evidence for Barlow’s observations by recording simultaneously from pairs of cat retinal ganglion cells (9). He found that nearby
cells exhibited correlated bursts of 2–3 spikes in darkness and that weak backgrounds increased the rate of these correlated bursts. Cross-correlation functions
calculated between the spike trains of two nearby cells had a slow component
with a timescale of ∼50 ms; this relatively long timescale is consistent with a
common input to the cell pair. Pairs of ON cells and pairs of OFF cells were positively
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correlated, and ON-OFF pairs were anticorrelated. These correlations included ON
and OFF center X (β or midget-like) and Y (α or parasol-like) ganglion cells.
On the basis of the similarity of the correlated bursts in darkness and in the presence of a dim background, Mastronarde argued that the slow correlations resulted
from photon-like events in the rods, either from spontaneous or light-activated
rhodopsin. Assuming a quantum efficiency of 0.12 and an approximate receptive
field size, Mastronarde estimated that each photon produced 1.5–2 spikes, similar
to the 2–3 spikes per burst estimated from autocorrelation functions computed
from the cell’s spike trains in darkness. Noise bursts occurred at a rate of 2–6 s−1 .
A rate of 6 s−1 corresponds to ∼0.002 events/rod/s given the rod density (24) and
receptive field size (25, 26). These conclusions are in agreement with those of
Barlow and colleagues (8).
The results from Mastronarde and Barlow are consistent with the idea that the
retina can detect and process single-photon responses. However, as with the behavioral data, this is not a unique interpretation. More conservatively, their results
argue for a source of discrete, independent noise events originating in the rods or
in the retinal circuitry. This noise likely originates in the AII amacrine cells or
earlier (see Retinal Circuitry and Interneurons, below) to explain the anticorrelation between ON and OFF cells. We provide additional arguments below that the
identification of the ganglion cell bursts with rod noise is tenuous.
Several studies provide evidence for nonadditive noise originating downstream
of the rods. Frishman & Levine (27) recorded from ganglion cells in the presence
of a steady dim background light or a modulated light with the same mean intensity. They showed that the statistics of ganglion cell spiking under these two
conditions were inconsistent with models with only photoreceptor noise. In addition, Lillywhite (19) and Saleh & Teich (28) argue that multiplicative noise can
account for many aspects of the ganglion cell signals previously attributed to additive noise. Multiplicative noise can explain the observation that the variance of
the ganglion cell spike count is greater than the mean (28). As with the behavioral
data, multiplicative noise may help explain why the inferred quantum efficiency
of ∼0.15 is lower than the absorptive quantum efficiency of ∼0.3 in cat (7, 17,
19). If each photon produces more than one spike, however, multiplicative noise
will play a lesser role in limiting sensitivity than Poisson fluctuations in photon
absorption. Furthermore, multiplicative noise alone cannot explain the ganglion
cell’s spontaneous spiking activity in darkness.
PHOTOTRANSDUCTION
The first prediction from behavioral and ganglion cell experiments, that rods can
detect single photons, was confirmed more than 25 years ago by Baylor and colleagues (1, 2). These studies identified three important functional properties of
the rod’s single-photon responses (e.g., Figure 2A): (a) The electrical response
triggered by photon absorption is highly amplified, (b) the rod maintains low noise
in darkness, and (c) individual single-photon responses show low trial-to-trial
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PHOTON DETECTION IN THE RETINA
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Figure 2 Single-photon responses and dark noise in a primate rod. (A) Responses to
a repeated dim flash producing an average of 0.5 Rh∗ . (B) Two sections of dark record
showing discrete and continuous dark noise. The average of eight discrete noise events
is compared with the cell’s average single-photon response in the inset. A section of
record in saturating light to eliminate the transduction current and isolate instrumental
noise is shown below.
variability. Combined efforts in biochemistry, molecular biology, and physiology
have since explored the molecular basis of each of these properties (29).
The activity of a single rhodopsin molecule is amplified in several stages [reviewed in (30)]. First, rhodopsin itself catalyzes the activation of hundreds or
thousands of copies of the G protein transducin (31, 32). Each transducin activates a single phosphodiesterase (efficiency ∼0.8), and each phosphodiesterase
hydrolyzes many cGMP molecules. This results in the closure of cGMP-gated
channels and a 1–2 pA reduction in inward current (2). In total, activation of a
single rhodopsin leads to the degradation of 105 –106 cGMP molecules and the
failure of ∼106 Na+ ions to flow into the outer segment. This scheme applies to
all vertebrate rods; however, the fidelity of the single-photon response varies considerably from one species to another. For instance, the signal-to-noise ratio of the
single-photon response is ∼6 in primate rods (2, 33) and ∼3 in mouse rods (34).
Dark noise in the rod signals comes from spontaneous activation of rhodopsin
and phosphodiesterase (2, 35, 36). Spontaneous rhodopsin activation produces
discrete noise events indistinguishable from single-photon responses (Figure 2B);
these occur once every 200–400 s in a monkey rod (2; G.D. Field & F. Rieke,
unpublished observation). Thus each of the rod’s ∼108 rhodopsin molecules activates spontaneously every 500–1000 years on average. Spontaneous phosphodiesterase activation produces continuous fluctuations in the rod current with a
magnitude about one fourth the size of the single-photon response (Figure 2B).
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These current fluctuations have a frequency composition similar to the rod’s singlephoton response, and thus occasional large continuous noise deviations will mimic
single-photon responses (2).
In addition to the low dark noise of the phototransduction cascade, the rod’s
single-photon responses vary little from one to the next, particularly when compared with the variations expected for signals initiated by a single molecule (33,
37, 38). This low variability poses an interesting molecular design question: How
is the activity of a single rhodopsin molecule regulated to avoid the expected statistical variations in its lifetime? Low variability is also required if the visual system
is to count photons, as suggested by Sakitt’s behavioral results, and may enable
the rod responses to encode precisely the time of photon arrival (37).
COMPARISON OF ROD NOISE WITH BEHAVIORAL AND
GANGLION CELL SENSITIVITY
Behavioral measurements and recordings from ganglion cells provide an estimate
of the intrinsic noise that limits absolute sensitivity. This noise has usually been
associated with spontaneous activation of rhodopsin, which produces additive Poisson noise (5, 8, 9). Indeed, the rate of photon-like noise events in monkey rods is in
approximate agreement with estimates of the noise limiting human behavior [reviewed in (18)]. Furthermore, the noise limiting the sensitivity of cat ganglion cells
consists of discrete bursts of spikes similar to those produced by dim backgrounds
(8, 9).
Several issues make the association of the dark light with photon-like noise
events in the rods tenuous. First, fits to the frequency of seeing curves depend
on several factors (quantum efficiency, additive Poisson noise, and multiplicative
Poisson noise) that can trade for one another (Figure 1). This precludes a unique
interpretation. Second, in the cat ganglion cell experiments, the number of rods
providing input to the recorded cells was not accurately determined, and the properties of noise in cat rods have not been measured. Experiments in toad provide
additional evidence that behavior approaches limits set by spontaneous rhodopsin
activation. In particular, the temperature dependence of behavioral threshold is
correlated with the temperature dependence of the rate of photon-like noise events
in the rods (39). However, the effect of temperature on behavior is very different in
frogs (40). This difference among closely related species, along with differences
in both the signal-to-noise ratio of the single-photon responses and the underlying retinal circuitry in amphibian and mammalian retina, makes it impossible to
generalize from toads to humans.
What other sources of noise might limit sensitivity? First, continuous noise in
mammalian rods can generate large fluctuations that look like true photon responses
(2, 34). Second, noise downstream of the rods, e.g., synaptic noise or noise in spike
generation, could contribute to either additive or multiplicative noise. Determining
the identity of the noise limiting ganglion cell sensitivity remains a key question
in how the retina works at low light levels. If the noise can be entirely attributed
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to the rods, the retinal readout of the rod array must be efficient and effectively
noiseless. This is a strong constraint.
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RETINAL CIRCUITRY AND INTERNEURONS
Behavioral, ganglion cell, and rod experiments illustrate that single photons are
detected at the initial stage of retinal processing and suggest that single-photon
responses reliably traverse the retina. Understanding the retinal mechanisms responsible for this sensitivity and the identity of the noise limiting sensitivity will
require tracking the rod’s single-photon responses across the retina. Our current understanding of how single-photon responses are processed by retinal interneurons
is primitive compared with our knowledge about rod and ganglion cell physiology.
Rod signals are transmitted across the mammalian retina through at least three
pathways: the rod bipolar pathway, the rod-cone pathway, and the rod-OFF pathway
(Figure 3; also see 41, 42). The rod bipolar pathway, which is a special feature of
mammalian retina, has long been considered the primary route for rod signals at
light levels near absolute threshold. Only recently has a solid experimental basis for
this idea been developed (43–45). In particular, isolation of the pathways through
a combination of genetic and pharmacological manipulations indicates that the
Figure 3 Pathways for rod signals. In mammalian retina, responses generated by
rod photoreceptors (R) can reach ganglion cells (GC) by three routes. In the rod bipolar pathway (46–48), rod signals are sent to rod bipolar cells (an ON-type bipolar)
(RB), which subsequently send these signals to a network of electrically coupled AII
amacrine cells. AII amacrines are electrically coupled to ON-cone bipolar (CB) cells
and connected by a glycinergic synapse to OFF-cone bipolar cells. These cone bipolars
send signals to ON and OFF ganglion cells. In the rod-cone pathway (24, 49, 50), rods
are electrically coupled to cones (C), which relay both ON and OFF signals to ganglion
cells through cone bipolars (CB). In the rod-OFF pathway (43, 51, 52), ganglion cells
receive rod input through OFF-cone bipolar cells, which make synaptic contacts with
rods.
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rod bipolar pathway operates at light levels at least 10-fold lower than the others.
Thus behavior at light levels near absolute threshold is mediated by the rod bipolar
pathway.
Rod signals in the rod bipolar pathway are passed to rod bipolar cells, a type
of ON or depolarizing bipolar cell that receives exclusive rod input (46–48). Rod
bipolars differ from prototypical bipolar cells because they make few or no direct
contacts with ganglion cells. Instead, each rod bipolar contacts several electrically
coupled AII amacrine cells (48). AII amacrine cells contact ON-cone bipolar cells
through gap junctions, and OFF-cone bipolar cells through sign-inverting glycinergic synapses. Cone bipolar cells transmit rod signals from the rod bipolar pathway
to ganglion cells. Below, we describe what is known about how each component of
this pathway processes single-photon responses and identify some of the outstanding open questions. A more detailed account of the anatomy and general function
can be found in Bloomfield & Dacheux (42).
The Rod-to-Rod Bipolar Synapse
Work on transmission of single-photon responses from rods to rod bipolar cells
has emphasized three issues: (a) the presynaptic mechanisms permitting reliable transmission of the small voltage changes produced by photon absorption,
(b) separation of the rod’s single-photon responses from continuous noise, and (c)
a speeding of the single-photon response in bipolar cells compared with that in rods.
Behavioral sensitivity requires that rods generate electrical responses to single
photons and that some of these responses are transmitted to rod bipolar cells. The
presynaptic hyperpolarization produced by absorption of a photon is only 1–2 mV
in amplitude (50). The small size of the presynaptic signal makes transmission
challenging compared with the situation at central synapses where the voltage
changes are 100 times larger. Rods are depolarized in darkness and continuously
release glutamate at a specialized ribbon-type synapse (53, 54). Photon absorption
hyperpolarizes the rod and produces a reduction or pause in release. Random
pauses or slowing generated by statistical fluctuations in release will masquerade
as true photon events and thus produce a source of noise potentially limiting visual
sensitivity (10). Such a noise source could account for some of the dark light
discussed in the Behavior and Ganglion Cell sections. The vesicle release rate and
statistics determine the magnitude of this synaptic noise.
Salamander rods release vesicles at a rate of at least 400 s−1 at the dark potential of −40 mV, as indicated by capacitance measurements (55); these rods,
however, have larger active zones and more release sites than mammalian rods.
Rao-Mirotznik and colleagues (12) argue that mammalian rods must maintain a
dark release rate of at least 80–100 s−1 to ensure that random pauses in release
produce fewer false photon-like events than spontaneous rhodopsin activation in
the rod outer segment. Their argument is based on two assumptions: Vesicle release obeys Poisson statistics, and release is completely abolished for ∼100 ms
during the single-photon response.
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PHOTON DETECTION IN THE RETINA
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The first assumption of Rao-Mirotznik (12) is supported by work on synapses
made by spiking cells. In particular, the release of vesicles at the neuromuscular
junction follows Poisson statistics as long as the release probability at each fusion
site is low (56); however, these are synapses that can reload between the brief bouts
of exocytosis produced by action potentials. The statistics of vesicle release could
be different when the synaptic machinery is forced to operate continuously as for
rods in darkness; each of the ∼40 fusion sites (11) in a mammalian rod spherule
would have to release several vesicles per second to support a rate of 100 s−1 .
Variability in vesicle release could be reduced if each vesicle fusion site exhibited a
refractory period following release, just as a refractory period can reduce variability
in spike generation (57, 58). Indeed, optical tracking of single vesicles in goldfish
bipolar terminals (also a ribbon-type synapse) indicates that they are not available
for exocytosis for ∼0.1 s after arrival at the membrane (59). If present, a similar
refractory period at release sites in the rod terminal could reduce the variance in
the number of vesicles released, thus reducing the required mean rate.
The second assumption made by Rao-Mirotznik and colleagues (12), that release is completely suppressed by the single-photon response, has not been directly
tested. This is because the small size of mammalian rods makes direct measurement of the voltage dependence of transmitter release difficult. This relation has
been studied in amphibians by recording simultaneously from rods and postsynaptic cells (60, 61), by monitoring capacitance changes produced by exocytosis (62),
and by detecting released glutamate photometrically (63). These studies indicate
that the release rate changes exponentially for a 2–5 mV voltage change. A similar
voltage dependence in mammalian rods would cause the 1–2 mV single-photon
response to suppress the rate of vesicle fusion by at most two thirds. Keeping
synaptic noise below rod outer segment noise in this case would require a dark release rate exceeding 400 s−1 . Direct measurement of the statistics of vesicle release
and the consequences of the resulting synaptic noise remains a key experiment.
A second issue at the rod-to-rod bipolar synapse is the separation of singlephoton responses from continuous noise. The continuous noise appears relatively
innocuous in recordings from single rods (e.g., Figure 2); however, when signals
from multiple rods are combined at light levels near absolute threshold, noise generated in all the rods threatens to overwhelm the light responses generated in a few
rods. Linear summation of the rod inputs is a poor strategy under these conditions
because each rod input contributes to the sum without regard to the likelihood
that the rod absorbed a photon. A more effective strategy is to identify rods that
likely absorbed a photon. This can be done by passing the rod inputs through
a thresholding nonlinearity that retains signals from those rods likely generating
single-photon responses and rejects signals from those rods likely generating noise
(2, 64) (see Figure 4). Convergence of 20–100 rods onto a rod bipolar makes the
rod-to-rod bipolar synapse the final opportunity to implement such a threshold.
Indeed, Field & Rieke (34) found that such a nonlinearity exists at the rod-torod bipolar synapse. As a consequence, the single-photon responses of mouse rod
bipolar cells are much more identifiable than those of the rods (Figure 4).
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Figure 4 Rod bipolar single-photon responses are more discrete than rod singlephoton responses. (A) Rod photocurrent and (B) rod bipolar current recorded while
delivering periodic flashes that generate ∼0.6 activated rhodopsins per rod. Rod currents were measured with a suction electrode. Rod bipolar currents were measured
under voltage clamp with the cell held at −60 mV. Histograms of the normalized response amplitudes are plotted in (C) rods and (D) rod bipolar cells. Each histogram
has been fit with a sum of weighted Gaussian functions.
Although convergence dictates the need for a thresholding nonlinearity at the
rod-to-rod bipolar synapse, it does not predict where the threshold should be positioned relative to the single-photon response. Instead, the optimal position of the
threshold is set by the light level and properties of the rod noise (Figure 5A,B). The
light level at visual threshold can be thought of as a prior probability of 0.0001
that an individual rod generates a single-photon response. Photon-like noise events
increase this probability to ∼0.0007 (for an 0.1 s integration time) because they
cannot be distinguished from real responses (these noise events were neglected in
Reference 34). Because of this, all signals smaller than the average single-photon
response in a mouse rod are more likely to be noise than signal and should be rejected. Thus only relatively large single-photon responses should be retained, i.e.,
those to the right of the crossing point between the signal and noise distributions in
Figure 5B. The observed threshold is close to this optimal position (34). Rejection
of single-photon responses sounds like a poor processing strategy; however, the
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PHOTON DETECTION IN THE RETINA
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Figure 5 Optimal separation of signal and noise by a thresholding nonlinearity.
(A) Comparison of the distribution of the continuous dark noise (thin line) and the
distribution of signals generated by single-photon responses plus the spontaneous activation of rhodopsin (thick line) for a rod photoreceptor at a light level of 0.6 Rh∗ . The
crossing point of the distributions (0.5) indicates the position of the optimal threshold.
Insets show the position of such a threshold relative to the rod responses. (B) Same as
(A) but at a light level of 0.0001 Rh∗ (near absolute threshold). The optimal position
of the threshold moves to a higher value as the light level is decreased. (C) Same comparison as (B) for a rod bipolar cell at visual threshold. The separation of signal and
noise in rod bipolar cells (see Figure 4) lowers the position of the optimal threshold
(0.75).
resulting decrease in noise dramatically improves the fidelity of the rod signals.
Rejection of some single-photon responses may contribute to the low quantum
efficiencies inferred from behavior and ganglion cell recordings when compared
with the absorptive quantum efficiency.
Electroretinograms (ERG) provide additional evidence for a nonlinearity in signal transfer from rods to rod bipolars (65, 66). The inferred position of the threshold
from ERG recordings is lower than that found in slice recordings, suggesting that
few photon responses are discarded at the rod-to-rod bipolar synapse. The position
inferred from ERG measurements implies one of the following scenarios: (a) The
threshold is not well placed to separate signal and noise; (b) the component of the
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ERG attributed to the rod bipolar cells does not effectively isolate events occurring
at the rod-to-rod bipolar synapse; or (c) existing rod noise measurements (2, 35,
36, 67) do not accurately describe rod noise in vivo. Because the ERG does not
measure rod noise, resolving this issue will require measuring signal and noise
from single cells under conditions close to those in vivo.
Sampath & Rieke (69) found that the thresholding nonlinearity at the rod-to-rod
bipolar synapse was produced by saturation of the G protein cascade in the rod
bipolar dendrites. Glutamate from the rods activates metabotropic receptors on
the rod bipolar dendrites, which leads to closure of nonselective cation channels
through a poorly understood signaling cascade (70). Reduction in glutamate release
during the rod light response reduces receptor activity, opens channels, and leads
to depolarization of the rod bipolar cell. In darkness, glutamate release in the rod
is sufficient to saturate a component of the rod bipolar transduction cascade and
cause the bipolar current to be insensitive to small changes in rod voltage and
the corresponding changes in glutamate release. Larger changes, such as those
produced during the single-photon response, relieve this saturation and produce
an electrical response in the rod bipolar cell.
The ability of a threshold to separate single-photon responses from noise requires that signals are not mixed between neighboring rods prior to reaching the
rod synaptic terminal. This is clearly not the case in amphibians, where rods display strong electrical coupling (71–74). Gap junctions are also present between
rods in some rodent retinas (52) but have not been described in cat or primate.
The discreteness of the single-photon responses in the rod bipolar currents and the
sensitivity inferred from behavior and ganglion cells suggest that gap junctions
between rods are not functional near absolute threshold (24).
A third aspect of signal transfer from rods to bipolar cells is the speeding
of the response in the bipolar cells compared with that of the rods (34, 65, 75)
(Figure 6). The response speeding indicates a reduced synaptic gain at low temporal
frequencies, i.e., a high-pass filtering. Indeed, paired recordings in amphibian retina
indicate that presynaptic mechanisms cause synaptic transmission between rods
and bipolars to preferentially transmit temporal frequencies near 2 Hz (68). This
filtering is well suited for the task of identifying the times of photon arrival on
the basis of the rod currents (76, 77), as temporal frequencies below 1 Hz carry
little information about photon arrival time and temporal frequencies above 4 Hz
are dominated by noise. This qualitative argument can be formalized to predict the
kinetics of the bipolar response based solely on the rod signal and noise. In mouse,
the kinetics of rod-mediated responses in retinal ganglion cells is only slightly
sped compared with that in the rod bipolars (Figure 6); thus the time course of
the rod signals is dominated by rod phototransduction and the rod-to-rod bipolar
synapse.
The work summarized in this section identifies the rod-to-rod bipolar synapse as
a key component of both transmission and processing of the rod responses. Not only
must this synapse maintain low noise, but linear and nonlinear mechanisms serve
to speed the rod single-photon responses and separate them from continuous noise
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PHOTON DETECTION IN THE RETINA
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Figure 6 (A) Normalized dim flash responses from a salamander rod and bipolar cell
recorded simultaneously (68). (B) Normalized dim flash responses from a mouse rod,
rod bipolar cell, and ON ganglion cell.
produced in rod phototransduction. Processing at the rod-to-rod bipolar synapse
appears to be well matched to the signal and noise properties of the rod signals.
The Rod Bipolar-to-AII Amacrine Synapse
Rod bipolar cells make ribbon synapses onto several types of amacrine cells (46),
but they have not been observed to contact ganglion cells directly. The connections
to the AII and A17 amacrine cells are the best studied. The A17 amacrines (S1/S2
amacrines in rabbit retina) modulate the rod signals by providing inhibitory feedback to rod bipolar synaptic terminals (78–82). Although the AII amacrines are a
requisite component of the rod bipolar pathway, little is known about how singlephoton responses traverse the rod bipolar-to-AII amacrine synapse. Below, we
focus on open questions at the rod bipolar-to-AII amacrine synapse, in particular
how the pattern of convergence and divergence affects photon detection.
AII amacrine cells in cat receive converging input from 20–30 rod bipolar cells,
each of which gets input from ∼15 rods (48). Thus the AII amacrine cell gets direct
input from ∼300–450 rods. At visual threshold, a small fraction (<0.01) of the
converging rod bipolar inputs to an AII amacrine carry a light-driven signal. Thus
the AII amacrine, similar to the rod bipolar, faces the problem of convergence of
sparse, noisy inputs. Convergence implies that a thresholding nonlinearity such as
that operating at the rod-to-rod bipolar synapse could serve to retain selectively
rod bipolar single-photon responses while rejecting noise (83). As is the case at
the rod-to-rod bipolar synapse, the best position of such a threshold is dictated by
the light level and the rod bipolar signal and noise, independent of convergence.
Noise in the rod bipolar inputs consists of fluctuations in the rod bipolar electrical signals (Figure 5C) and noise produced by stochastic fluctuations in vesicle
fusion at the synapse. These noise sources, particularly the synaptic noise, have
not been characterized sufficiently to determine how severely linear summation
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by the AII amacrine would compromise sensitivity. Figure 5C gives a rough estimate of the distributions of photon-like events and noise in the rod bipolar cell
at absolute threshold assuming synaptic noise is negligible. The separation of the
signal and noise distributions is greater than that in the rod responses (Figure
5B,C); thus an optimally positioned threshold at the rod bipolar-to-AII synapse
(arrow, Figure 5C) would reject a smaller fraction of the single-photon responses
than are rejected at the rod-to-rod bipolar synapse. Figure 5C suggests that at
absolute visual threshold the smallest 10–15% of the rod bipolar single-photon
responses would have a greater probability of being noise than signal and should
be discarded. This nonlinear summation would improve the signal-to-noise ratio
of the AII signals by a factor of ∼20 compared with linear summation. There is at
present no evidence for or against a threshold at the rod bipolar-to-AII amacrine
synapse.
Divergence causes the signal from a single rod to be represented in several
AII amacrine cells. A single rod typically sends outputs to 2 rod bipolar cells,
and these 2 rod bipolar cells contact ∼5 AII amacrine cells (48). This divergence
will correlate light-dependent signals in nearby AIIs. Noise generated at the rod
bipolar-to-AII synapse should be independent in different AII amacrines, however,
and thus the fidelity of the rod signal may be improved by averaging across AIIs
(83, 84). Such averaging is provided by electrical coupling between nearby AII
amacrines (85–87), which causes the signals to spread from 1 AII amacrine to
∼20 neighbors under dark-adapted conditions (88). AII amacrine cells, unlike
rods and rod bipolar cells, can also generate Na+ action potentials (89). Modeling
work suggests that the resulting nonlinear electrical properties may amplify singlephoton responses more than noise in a collection of electrically coupled AIIs (83).
The role of these action potentials in signal transmission at visual threshold has
not been determined experimentally.
The AII-to-Cone Bipolar and Cone Bipolar-to-Ganglion
Cell Synapses
Single-photon responses in the AII amacrine cell network are passed to ON- and
OFF-cone bipolar cells through distinct mechanisms. ON signals from AII amacrine
cells pass through gap junctions to ON-cone bipolar cell terminals. Electrical signaling through these gap junctions preferentially transmits signals from AII amacrines
to cone bipolars owing to the large difference (∼fivefold) in input impedance of
the AII amacrines and the cone bipolar cells (87). Signals from AII amacrines are
passed to OFF-cone bipolar cell terminals through inhibitory glycinergic synapses
(90). The properties of glycinergic signal transmission from AII amacrines to OFFcone bipolar cells are not well understood. Connections to ON and OFF bipolars do
not appear to be entirely promiscuous because some ON and OFF ganglion cells receive almost exclusive cone input (44, 45, 91), requiring bipolar cells that get little
or no rod input (92). This also suggests that some cones do not make functional
gap junctions with rods (see Figure 3).
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Cone bipolar cells convey rod signals directly to ganglion cells. Because all
rod signals are conveyed to ganglion cells through cone bipolar cells, no ganglion
cells should receive exclusive rod input. Although the cone bipolar-to-ganglion
cell synapse has not been studied at light levels near absolute threshold, a good
deal is known about how these synapses operate at cone light levels [reviewed in
(93)].
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CONCLUSIONS
We have reviewed what is currently known about retinal processing near absolute
visual threshold. Behavioral experiments (3, 6, 7) and in vivo retinal ganglion cell
recordings (8, 9) predict that rod photoreceptors can detect single photons and
indicate a source or soures of additive Poisson noise that can be expressed as an
equivalent input light or dark light (2). Some of the dark light clearly originates
from spontaneous activation of rhodopsin in the rods. However, attributing all of
the dark light to this source causes the behavioral and absorptive quantum efficiencies to differ substantially. Imposing that the quantum efficiencies are similar
requires a large increase in the dark light, making it unlikely that rod noise alone
limits sensitivity. These possibilities provide two very different views of retinal
processing: The first implies the rod signals are efficiently and noiselessly read
out by the retinal circuitry, the other implies that a significant amount of additive
noise is introduced post-photoreceptor.
The comparison of the noise limiting behavior with noise from spontaneous
activation of rhodopsin neglects at least three issues. First, such a comparison
fails to consider other sources of noise in the rods, particularly continuous noise
(35, 36). Single-photon responses can be separated from continuous noise by
a threshold-like nonlinearity at the rod-to-rod bipolar synapse (2, 64). Such a
threshold, however, would also reject a substantial fraction of the rod’s singlephoton responses (34) and thus contribute to limiting sensitivity. This may be
one of the reasons that the absorptive and behavioral quantum efficiencies differ.
Second, the assumption that the intrinsic noise can be modeled as an additive
Poisson source may be wrong. A combination of additive and multiplicative noise
(7, 19) can explain behavioral and ganglion cell sensitivity while providing closer
agreement of behavioral and absorptive quantum efficiencies. Third, there is more
to rod vision than detecting the presence or absence of dim lights. For example,
estimating motion relies on extracting temporal information from the rod array.
The noise limiting such computations may differ from that limiting detection.
Reproducibility of the rod’s single-photon responses may permit photon arrival
times to be encoded precisely (37), and bandpass filtering at the rod synapse may
be a first step in extracting this temporal information (68, 76).
To date, the main success story in our understanding of the retinal basis of
photon detection has been work establishing how rod photoreceptors detect single
photons. The same sort of quantitative and mechanistic description is lacking in
our understanding of how the retina reads out the rod signals. We are beginning to
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understand how the first synapse in the retina may be optimized for processing retinal signals, but even here significant gaps in our knowledge remain. In particular,
we know neither the statistics of vesicle release nor how the rod signals are being
sped. Our knowledge of how single-photon responses are processed at subsequent
synapses and encoded in the electrical activity of downstream retinal interneurons
is even more incomplete. In addition, recent anatomical and physiological work has
identified alternative pathways that rod signals can take through the mammalian
retina. The function of these additional pathways is not clear, although a possible
explanation is that they serve to process the rod signals over a wide range of light
levels (41).
Vision is one of several examples in neurophysiology where sensory performance is exquisitely sensitive [reviewed in (94)]. Pheromone detection in insects
may approach the single-molecule limit (95). Hearing approaches sensitivity limits
set by thermal motion of the auditory hair cell stereocilia (94). Electroreceptors can
detect voltage gradients of ∼10 nV/cm (96), much smaller than voltage changes required to open most ion channels. These examples of extreme sensitivity challenge
our understanding of sensory transduction, processing, and encoding. Studies of
photon detection in the retina provide an opportunity to understand how the underlying biophysical mechanisms meet these challenges.
ACKNOWLEDGMENTS
We thank Horace Barlow, E.J. Chichilnisky, Thuy Doan, Felice Dunn, and
Valerie Uzzell for valuable comments on the manuscript, and W. Rowland Taylor
and Robert Smith for helpful discussions. Support was provided by the National
Institutes of Health through grant EY-11850 (FR) and NRSA EY-14784 (APS).
G.D. Field and A.P. Sampath contributed equally to this review.
The Annual Review of Physiology is online at
http://physiol.annualreviews.org
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Annual Review of Physiology
Volume 67, 2005
CONTENTS
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Frontispiece—Michael J. Berridge
xiv
PERSPECTIVES, Joseph F. Hoffman, Editor
Unlocking the Secrets of Cell Signaling, Michael J. Berridge
Peter Hochachka: Adventures in Biochemical Adaptation,
George N. Somero and Raul K. Suarez
1
25
CARDIOVASCULAR PHYSIOLOGY, Jeffrey Robbins, Section Editor
Calcium, Thin Filaments, and Integrative Biology of Cardiac Contractility,
Tomoyoshi Kobayashi and R. John Solaro
39
Intracellular Calcium Release and Cardiac Disease, Xander H.T. Wehrens,
Stephan E. Lehnart and Andrew R. Marks
69
CELL PHYSIOLOGY, David L. Garbers, Section Editor
Chemical Physiology of Blood Flow Regulation by Red Blood Cells:
The Role of Nitric Oxide and S-Nitrosohemoglobin, David J. Singel
and Jonathan S. Stamler
RNAi as an Experimental and Therapeutic Tool to Study and Regulate
Physiological and Disease Processes, Christopher P. Dillon,
Peter Sandy, Alessio Nencioni, Stephan Kissler, Douglas A. Rubinson,
and Luk Van Parijs
99
147
ECOLOGICAL, EVOLUTIONARY, AND COMPARATIVE PHYSIOLOGY,
Martin E. Feder, Section Editor
Introduction, Martin E. Feder
Biophysics, Physiological Ecology, and Climate Change: Does Mechanism
Matter? Brian Helmuth, Joel G. Kingsolver, and Emily Carrington
Comparative Developmental Physiology: An Interdisciplinary
Convergence, Warren Burggren and Stephen Warburton
Molecular and Evolutionary Basis of the Cellular Stress Response,
Dietmar Kültz
175
177
203
225
ENDOCRINOLOGY, Bert O’Malley, Section Editor
Endocrinology of the Stress Response, Evangelia Charmandari,
Constantine Tsigos, and George Chrousos
259
vii
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CONTENTS
Lessons in Estrogen Biology from Knockout and Transgenic Animals,
Sylvia C. Hewitt, Joshua C. Harrell, and Kenneth S. Korach
285
Ligand Control of Coregulator Recruitment to Nuclear Receptors,
Kendall W. Nettles and Geoffrey L. Greene
309
Regulation of Signal Transduction Pathways by Estrogen
and Progesterone, Dean P. Edwards
335
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GASTROINTESTINAL PHYSIOLOGY, John Williams, Section Editor
Mechanisms of Bicarbonate Secretion in the Pancreatic Duct,
Martin C. Steward, Hiroshi Ishiguro, and R. Maynard Case
+
+
377
Molecular Physiology of Intestinal Na /H Exchange,
Nicholas C. Zachos, Ming Tse, and Mark Donowitz
411
Regulation of Fluid and Electrolyte Secretion in Salivary Gland
Acinar Cells, James E. Melvin, David Yule, Trevor Shuttleworth,
and Ted Begenisich
445
Secretion and Absorption by Colonic Crypts, John P. Geibel
471
NEUROPHYSIOLOGY, Richard Aldrich, Section Editor
Retinal Processing Near Absolute Threshold: From Behavior
to Mechanism, Greg D. Field, Alapakkam P. Sampath, and Fred Rieke
491
RENAL AND ELECTROLYTE PHYSIOLOGY, Gerhard H. Giebisch, Section Editor
A Physiological View of the Primary Cilium, Helle A. Praetorius
and Kenneth R. Spring
515
Cell Survival in the Hostile Environment of the Renal Medulla,
Wolfgang Neuhofer and Franz-X. Beck
531
Novel Renal Amino Acid Transporters, Francois Verrey, Zorica Ristic,
Elisa Romeo, Tamara Ramadam, Victoria Makrides, Mital H. Dave,
Carsten A. Wagner, and Simone M.R. Camargo
557
Renal Tubule Albumin Transport, Michael Gekle
573
RESPIRATORY PHYSIOLOGY, Carole R. Mendelson, Section Editor
Exocytosis of Lung Surfactant: From the Secretory Vesicle to the
Air-Liquid Interface, Paul Dietl and Thomas Haller
Lung Vascular Development: Implications for the Pathogenesis of
Bronchopulmonary Dysplasia, Kurt R. Stenmark and Steven H. Abman
623
Surfactant Protein C Biosynthesis and Its Emerging Role in
Conformational Lung Disease, Michael F. Beers and Surafel Mulugeta
663
595
SPECIAL TOPIC, CHLORIDE CHANNELS, Michael Pusch, Special Topic Editor
Cl− Channels: A Journey for Ca2+ Sensors to ATPases and Secondary
Active Ion Transporters, Michael Pusch
697
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CONTENTS
ix
Assembly of Functional CFTR Chloride Channels, John R. Riordan
701
Calcium-Activated Chloride Channels, Criss Hartzell, Ilva Putzier,
and Jorge Arreola
719
Function of Chloride Channels in the Kidney, Shinichi Uchida
and Sei Sasaki
759
Physiological Functions of CLC Cl− Channels Gleaned from Human
Genetic Disease and Mouse Models, Thomas J. Jentsch,
Mallorie Poët, Jens C. Fuhrmann, and Anselm A. Zdebik
779
Structure and Function of CLC Channels, Tsung-Yu Chen
809
INDEXES
Subject Index
Cumulative Index of Contributing Authors, Volumes 63–67
Cumulative Index of Chapter Titles, Volumes 63–67
ERRATA
An online log of corrections to Annual Review of Physiology chapters
may be found at http://physiol.annualreviews.org/errata.shtml
841
881
884
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