Ординатура / Офтальмология / Английские материалы / Eye, Retina, and Visual System of the Mouse_Chalupa, Williams_2008

van der Horst, G. T., Muijtjens, M., Kobayashi, K., Takano, R., Kanno, S., Takao, M., de Wit, J., Verkerk, A., Eker, A. P., et al. (1999). Mammalian Cry1 and Cry2 are essential for maintenance of circadian rhythms. Nature 398(6728):627– 630.

Van Gelder, R. N. (2001). Non-visual ocular photoreception. Ophthalmic Genet. 22(4):195–205.

Van Gelder, R. N., Wee, R., Lee, J. A., and Tu, D. C. (2003). Reduced pupillary light responses in mice lacking cryptochromes. Science 299(5604):222.

Warren, E. J., Allen, C. N., Brown, R. L., and Robinson, D. W. (2003). Intrinsic light responses of retinal ganglion cells projecting to the circadian system. Eur. J. Neurosci. 17(9):1727– 1735.

Waterhouse, J. M., and DeCoursey, P. J. (2004). The relevance of circadian rhythms for human welfare. In J. C. Dunlap, J. J. Loros, and P. J. DeLaney (Eds.), Chronobiology: Biological timekeeping

(pp. 325–358). Sunderland, MA: Sinauer.

Wee, R., Castrucci, A. M., Provencio, I., Gan, L., and Van Gelder, R. N. (2002). Loss of photic entrainment and altered free-running circadian rhythms in math5−/− mice. J. Neurosci. 22(23):10427–10433.

Welsh, D. K., Logothetis, D. E., Meister, M., and Reppert, S. M. (1995). Individual neurons dissociated from rat suprachi-

asmatic nucleus express independently phased circadian firing rhythms. Neuron 14(4):697–706.

Xu, Y., Toh, K. L., Jones, C. R., Shin, J. Y., Fu, Y. H., and Ptacek, L. J. (2007). Modeling of a human circadian mutation yields insights into Clock regulation by PER2. Cell 128(1): 59–70.

Yamazaki, S., Goto, M., and Menaker, M. (1999). No evidence for extraocular photoreceptors in the circadian system of the Syrian hamster. J. Biol. Rhythms 14(3):197–201.

Yan, L., and Silver, R. (2004). Resetting the brain clock: Time course and localization of mPER1 and mPER2 protein expression in suprachiasmatic nuclei during phase shifts. Eur. J. Neurosci. 19(4):1105–1109.

Yoo, S. H., Yamazaki, S., Lowrey, P. L., Shimomura, K., Ko, C. H., Buhr, E. D., Siepka, S. M., Hong, H. K., Oh, W. J., et al. (2004). PERIOD2::LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proc. Natl. Acad. Sci. U.S.A. 101(15): 5339–5346.

Yoshimura, T., and Ebihara, S. (1996). Spectral sensitivity of photoreceptors mediating phase-shifts of circadian rhythms in retinally degenerate CBA/J (rd/rd) and normal CBA/N (+/+) mice.

J. Comp. Physiol. A 178(6):797–802.

The dorsal lateral geniculate nucleus (dLGN) plays a key role in the mouse brain’s processing of the visual world. Positioned in the flow of information from eye to cortex, its most fundamental job is to provide the neocortex with visual input. It does this not only by passing on the results of processing achieved in the retina but also by introducing more complexity and flexibility into the visual signal—the dLGN is not a passive relay, and reshapes retinal information in important ways. Studying the physiology of this important processing station, then, is key to understanding higher vision. Indeed, it is impossible to understand cortical visual physiology without first knowing the properties of its major driving input. This need for information about geniculate physiology is even more marked in the mouse, since mutation-induced changes in cortical visual function can only be safely interpreted if we know their effects on dLGN function, too (e.g., Rossi et al., 2001; Grubb et al., 2003). To use the mouse to understand higher visual processing, then, we need to understand the physiology of the mouse dLGN. It is a vital link in our knowledge of how the mouse visual system functions.

There is another good reason to study mouse dLGN physiology: the dLGN is the best understood nucleus of the thalamus and has long been used as a model for thalamic function as a whole. This means that questions of mouse dLGN physiology and function are intimately bound up with basic issues of how the thalamus works. We know the thalamus acts as a relay, and appears to change its relayed information in subtle ways, but how exactly? The dLGN is considered a first-order thalamic relay, receiving driving inputs from subcortical structures and sending them on to cortex (e.g., Sherman and Guillery, 2003). As such, it is in a minority within the thalamus, with most nuclei being higher order and subserving communication between different areas within cortex. However, the dLGN is still a good model for general thalamic physiology: almost all of its basic intrinsic and circuit-level function is mirrored in the known properties of other thalamic nuclei (e.g., Sherman, 2005). Furthermore, in any search for a mouse model of primate thalamic function, the dLGN is by far the best candidate. Like all thalamic nuclei in primates, but unlike all other thalamic nuclei in mice, the mouse dLGN contains both

glutamatergic relay cells and GABAergic local interneurons (Arcelli et al., 1997). Studying mouse dLGN physiology, therefore, is useful not only when one is trying to understanding mouse vision. It also provides important information about general thalamic function.

This chapter describes our current knowledge of mouse dLGN physiology, from the level of intrinsic cell physiology up to the visual response properties of mouse dLGN neurons. Well aware that many of the most insightful visual and cellular physiology studies of the dLGN were performed in other species, I try to be comparative as much as possible; in many cases it is a matter of showing that the mouse dLGN is just like, or slightly different from, other cases we already know well. However, this situation should change in the future. The availability of powerful genetic manipulations in the mouse should make the mouse dLGN a leading site for studies relating visual and thalamic physiology to the action of individual proteins, and indeed this is already starting to happen. So, whereas carnivore and primate physiology currently informs and shapes our knowledge of the mouse dLGN, the flow of information should soon be headed at least partly in the opposite direction. In this spirit, throughout this chapter I highlight some areas in which the mouse dLGN could lead the way in our understanding of both thalamic and visual physiology.

Intrinsic physiology of mouse dLGN neurons

Understanding the visual role of the mouse dLGN requires basic knowledge about the intrinsic physiological properties of its neurons. Any visual computation taking place in the nucleus will, after all, fundamentally depend on changes in the ionic currents flowing into and out of mouse dLGN cells. Indeed, many of the intrinsic features of mouse dLGN neurons are rather specialized for thalamic functioning. This section describes the basic ionic currents possessed by mouse dLGN cells, concentrating on thalamocortical relay cells, where almost all of our knowledge is based. The species focus in this field has largely been on other rodents (guinea pig, rat) and carnivores (cat, ferret), but we are beginning to develop a good picture of how mice compare. Indeed, genetic manipulations are becoming increasingly useful for

studying the molecular bases of intrinsic geniculate physiological properties. The good news is that the main features of intrinsic thalamic physiology seem extremely well conserved, both across individual thalamic nuclei within a given species and across different species and even different phyla (Llinas and Steriade, 2006), so that what we know about the mouse dLGN largely applies to other animals, and vice versa.

Sodium Currents Like most neurons in the brain, those in the mouse dLGN possess a fast transient sodium current (INa,t), which activates and then inactivates rapidly at depolarized membrane potentials (e.g., MacLeod et al., 1997; Jaubert-Miazza et al., 2005; figure 18.1). This current underlies the firing of rapid action potentials, which among other functions sends visual information out of the dLGN toward cortex.

Calcium Currents The presence of a particular calcium current, IT, is a hallmark of thalamocortical relay cells (TCs) and underlies some of their most important physiological functions. The current, like the transient sodium current underlying action potentials, is activated and then inactivated at depolarized membrane potentials, although IT kinetics are much slower, and IT needs less depolarization to activate (it is a low-threshold calcium current). The main effect of IT is to produce low-threshold calcium spikes (LTSs), which

Figure 18.1 Burst and tonic firing modes in a mouse dLGN neuron. The figure shows thalamocortical cell responses in current clamp following stimulation of RGC axons in the optic tract (arrowhead). Top, When the cell is relatively depolarized, at −55 mV, RGC stimulation produces an EPSP accompanied by a single sodium spike. Bottom, When the cell is relatively hyperpolarized, at −75 mV, RGC stimulation produces an EPSP accompanied by a lowthreshold calcium spike. A burst of sodium action potentials rides the crest of this LTS. (From Blitz and Regehr, 2003.) Scale bar applies to both traces.

depolarize membrane voltage to levels at which INa,t is activated, resulting in a burst of sodium spikes riding on a slower calcium LTS (Jahnsen and Llinas, 1984; see figure 18.1). This firing pattern (burst mode) is very different from that produced by depolarization in the absence of IT activation, which produces trains of action potentials with much longer interspike intervals (tonic mode). It is this distinction between burst and tonic TC activity that may be crucial in determining the geniculate relay of visual information at any given point in time.

The slow nature of IT kinetics has important implications for the circumstances in which it is activated. Once inactivated at depolarized membrane potentials (a process that takes ca. 100 ms), the cell membrane must undergo hyperpolarization for about 100 ms or more before IT channels become deinactivated and ready for re-use. This means that control of IT is achieved through slow-acting modulation that can alter membrane voltage for periods of 100 ms or more. In this way modulatory metabotropic neurotransmission can regulate IT activity, and thus TC firing mode, in ways that fast ionotropic neurotransmission cannot.

IT has been identified in all TCs in all species studied to date (Sherman and Guillery, 2003), and the mouse is no exception (MacLeod et al., 1997; Blitz and Regehr, 2003; Jaubert-Miazza et al., 2005; Meuth et al., 2006a, 2006b). Possible functional implications of burst versus tonic firing modes, and the modulation of these two response states in the mouse dLGN, are discussed in detail in the pages that follow.

Mouse dLGN cells also have other, less well-studied calcium currents. High-threshold calcium spikes through IL/N currents, crucial to fully explaining TC cell responses under depolarization (Rhodes and Llinas, 2005), have been identified in the mouse dLGN (MacLeod et al., 1997), while “plateau” potentials arising through activation of l-type calcium channels have also been described in a mouse dLGN preparation, although these figured predominantly in immature developmental stages (Jaubert-Miazza et al., 2005).

Potassium Currents A wide range of potassium currents shapes the subthreshold and action potential firing capabilities of all central neurons, including mouse TCs. The rectifying current IK, which is activated at highly depolarized membrane voltages and which helps, among other functions, the repolarization following sodium spikes (McCormick, 2003), has been clearly identified in mouse TCs in vitro (MacLeod et al., 1997; Jaubert-Miazza et al., 2005). Mouse dLGN cells also possess a slow afterhyperpolarization potassium current (IAHP; Jaubert-Miazza et al., 2005), which can act as a brake on repetitive sodium spike firing (McCormick, 2003). In addition, mouse TCs are known to possess the IA potassium current (MacLeod et al., 1997), which activates and inactivates rapidly on membrane depolarization and can

therefore slow or delay the firing of both sodiumand calcium-based spikes (Huguenard and McCormick, 1992). Finally, a leak potassium current IK,leak, which contributes to the resting membrane potential of TCs, has been well described in the mouse dLGN; in fact, genetic manipulations in murine models have proved crucial in understanding the particular TASK channels that underlie this current (Meuth et al., 2006a).

Hyperpolarization-Activated Currents Ih is a mixed cation current that activates at hyperpolarized membrane potentials and acts to depolarize the cell. Its presence in TCs has been well described because of the role it can play, in conjunction with IT, in setting up intrinsic slow oscillations in thalamic neurons. LTSs produced by IT activation wane once IT is inactivated, producing a membrane hyperpolarization that can activate Ih, thus depolarizing the cell again and providing enough activation for another IT-based LTS (e.g., Huguenard and McCormick, 1992; McCormick and Bal, 1997). Ih is certainly present in mouse dLGN cells (Meuth et al., 2006a), where, along with potassium leak currents, it plays a key role in setting resting membrane potential levels. The presence or absence of slow oscillations produced from the combined actions of Ih and IT, however, has not been directly addressed in the mouse geniculate.

Oscillations in the Mouse dLGN? As described earlier, particular combinations of membrane currents possessed by thalamic cells can produce oscillatory activity. Important oscillations are also set up in the dLGN by interactions between TCs and cells in other brain areas, including cortex and the thalamic reticular nucleus (TRN; McCormick and Bal, 1997). These oscillations are prevalent in the naturally sleeping or anesthetized brain, and thalamic/dLGN cells are known to be a vital part of the circuits producing normal and pathological network oscillations (e.g., Llinas and Steriade, 2006). Indeed, one vital function of the dLGN may be to regulate the flow of visual information during different states of alertness by altering rhythmic activity patterns. However, the seminal work describing these oscillations has occurred exclusively in non-mouse mammalian models, and although the mouse dLGN appears to have all the intrinsic currents, cells, and circuits necessary to oscillate, it is unclear whether it has the same network behavior. There has been one report of slow, approximately 1 Hz oscillations in extracellularly recorded mouse dLGN spikes in vitro (Zhu et al., 2006), but we need to build on these initial data before we can use the full power of the mouse model to understand the basis, and maybe even the functional implications, of these oscillations in health and disease.

Overall, then, mouse dLGN cells are model examples of thalamic units, with no great differences across species.

There are gaps left to fill in our knowledge, of course, not least concerning oscillatory activity. It is also unclear whether mouse dLGN cells have other important currents— including IK2 and INa,p—that have been identified in the TCs of other species (e.g., Jahnsen and Llinas, 1984; Huguenard and McCormick, 1992). Furthermore, it is important to note that our knowledge of intrinsic mouse dLGN physiology comes almost entirely from TC recordings, and although we know there are important differences between TCs and local interneurons in terms of intrinsic capabilities in other species (e.g., McCormick and Bal, 1997; Llinas and Steriade, 2006), nothing is known about these differences in mice. This knowledge would be particularly useful insofar as the dLGN is the only thalamic nucleus in the mouse to possess GABAergic interneurons (Arcelli, et al., 1997). Nevertheless, as the case of resting membrane potential shows (Meuth et al., 2006a, 2006b), using the mouse dLGN as a model for these intrinsic properties has great value, allowing molecular-level understanding of crucial electrophysiological features.

Relaying the retinal signal

The intrinsic properties of mouse dLGN cells combine with the input received by the nucleus from various sources to produce the dLGN’s output—sodium spikes transmitted to visual cortex. And although the majority of synapses received by TCs come from nonretinal sources, the roughly 7% of synapses that derive from the eye (Sherman, 2005) dominate the visual physiology of the dLGN—they are “driving” inputs. At least in part, then, the mouse dLGN serves to relay visual information from the eye to cortex. How it does so and the consequences for the visual response properties of mouse dLGN neurons are described in this section.

Retinogeniculate Inputs and Spatial Information In vitro recordings have shown that mouse TCs receive input from very few (one to three) individual retinal ganglion cell (RGC) fibers, and that these inputs always arise from the same eye (Chen and Regehr, 2000; Jaubert-Miazza et al., 2005; figure 18.2). Insofar as these inputs are also strong, with high release probability (Chen and Regehr, 2000; Reichova and Sherman, 2004; Kielland et al., 2006), one might expect the spatial visual response properties of mouse dLGN neurons to reflect very closely those of their individual RGC inputs. This is certainly the case in other species (Hubel and Wiesel, 1961; Usrey et al., 1999), and is true in the mouse, where dLGN cells all respond to visual stimulation through one eye only (Metin et al., 1983; Grubb et al., 2003), and share many important spatial properties with their RGC input. In particular, the receptive fields (RFs) of most mouse dLGN cells, like those in other mammalian species (e.g., Hubel and Wiesel, 1961; Wiesel and Hubel, 1966), are

Figure 18.2 Features of retinogeniculate inputs in the mouse. A, Mature mouse dLGN neurons receive input from one or few RGCs. Left plot shows retinogeniculate EPSC amplitude, for both NMDA events (open triangles) and for AMPA events (filled circles), as a function of increasing RGC stimulation amplitude. Right plot shows whole-cell-recorded EPSC traces at +40 mV (AMPA + NMDA; top) and −70 mV (AMPA only; bottom) after RGC stimulation of varying amplitude. Both plots show clearly that increasing the number of stimulated RGC fibers produces an all-or-nothing step in postsynaptic response size, consistent with a single presynaptic RGC input to the recorded cell. B, AMPA and NMDA events in mouse dLGN neurons differ in the temporal patterns produced by RGC inputs. Plots show NMDA EPSCs (top; +40 mV in NBQX) and AMPA EPSCs (middle; −70 mV in CPP) in a mouse thalamocortical neuron during stimulation of RGC inputs with a pattern mimicking a mouse RGC’s response to a flash of light (bottom). Notice the large diminution in AMPA current produced by highfrequency RGC inputs, in contrast to the sustained level of NMDA activation in such circumstances. Scale bar applies to both top and middle traces. (A, Modified from Chen and Regehr, 2000. B, Modified from Chen et al., 2002.)

circular, concentric, and center-surround in organization (figure 18.3). They can be ONor OFF-center, so responding best to either a light spot on a dark background (ON-center) or a dark spot on a light background (OFF-center; Grubb and Thompson, 2003). The mouse retina contains cells of this type too (e.g., Balkema and Pinto, 1982; Tian and Copenhagen, 2003), although the question of whether particular types of mouse RGC project only to dLGN

neurons with the same RF type (see Usrey et al., 1999) has yet to be definitively examined. It is also important to note that center-surround RFs are very probably not the only RF type present in either mouse retina or dLGN (they represented ca. 60% of a dLGN sample; Grubb and Thompson, 2003); for the moment, they simply represent the population we know the most about. However, given the physiology of the retinogeniculate projection, one would also expect the RF features of other mouse RGC types to be mapped fairly faithfully onto their geniculate targets. This is probably the case in cats and in primates, where some W-type or koniocellular (K) dLGN neurons, respectively, possess non- center-surround RFs (e.g., Sur and Sherman, 1982; Xu et al., 2001).

Not only are there similarities in RF structure between the mouse retina and dLGN, there is also great similarity in RF size. RF center diameter in mouse RGCs is about 5–10° (Balkema and Pinto, 1982), while the mean value in mouse dLGN, calculated from spatial frequency (SF) tuning curves, is about 6° (Grubb and Thompson, 2003). Center size largely predicts a cell’s spatial acuity, reflected in its SF cutoff frequencies, and there the similarities continue: the average mouse dLGN cell can resolve an SF of 0.18 c/deg, and maximum acuity values in the nucleus are about 0.5 c/deg. In comparison, mouse RGCs have SF cutoffs of around 0.2 c/deg, while the ERG response of the retina as a whole has a spatial acuity (reflective of that of its best-performing units) of about 0.6 c/deg. It should be apparent from this discussion that, comparatively, mouse vision at the thalamic level is extremely poor in terms of spatial acuity: macaque dLGN RF centers are normally less than 1° in diameter, for example, leading to an acuity of about 40 c/deg (e.g., Merigan et al., 1991; figure 18.4). However, this poor performance cannot be blamed on the mouse dLGN. It is simply making the most of the low-resolution information it receives from the retina.

The spatial signal relayed by dLGN cells can depend on the particular type of cell that is doing the relaying. In primates, three main classes of geniculate cell have been identified—parvocellular (P), magnocellular (M), and koniocellular (K)—based on a multitude of distinguishing biochemical, anatomical, and physiological characteristics. In terms of spatial response characteristics, P cells have smaller RFs than M cells, which in turn have smaller RFs than K cells (e.g., Xu et al., 2002). Most RFs in all classes, however, are center-surround in organization and summate spatial influences linearly across their RFs (e.g., Xu et al., 2001). These features, at least for P and M cells, are likely to stem directly from the response properties of different classes of primate RGC (Callaway, 2005). Similarly, the cat dLGN also contains cell populations that differ in their spatial response characteristics. X cells have smaller RFs than Y cells, which in turn have smaller RFs than W cells (e.g., Sur

Figure 18.3 Representative visual response properties of mouse dLGN neurons. A, Mouse dLGN cell receptive fields (RFs), mapped from responses to localized flashed black-and-white square stimuli. The resultant circular, concentric RFs are typical of most mouse dLGN cells and are either ON-center (top) or OFF-center (bottom). B, Spatial frequency (SF) tuning. Cell response magnitude is plotted against the SF of drifting sinusoidal grating stimuli. Like this cell, most mouse dLGN neurons prefer SFs of about 0.02 c/deg, and have SF cutoffs of about 0.2 c/deg. C, Linearity of spatial summation. Cell response amplitude at both the first (F1) and second (F2) harmonics of the stimulus is plotted against the phase of stationary, counterphased sinusoidal gratings. Two null positions, where the

and Sherman, 1982). Most cells in all groups have centersurround RF organization, but the linearity of summation differs among the different classes: X cells are linear, Y cells display nonlinear doubling responses at high SFs, and the W cell population shows examples of both types of behavior (Sur and Sherman, 1982). Again, these differences are likely to arise directly from the spatial properties of the retinal input: X and Y cells in particular are known to receive strong inputs only from the corresponding RGC type (Usrey et al., 1999). Evidence for different functional cell classes in the mouse dLGN, however, is lacking (Grubb and Thompson, 2003). RF sizes in our sample did not segregate into distinct groups, whereas almost all mouse dLGN cells displayed linear spatial summation. This latter finding was surprising, given that both linear and nonlinear summation have been observed in mouse RGCs (Balkema and Pinto, 1982). However, it is possible that “Y-like” mouse RGCs project to nongeniculate targets, or that they project (weakly) to postsynaptic targets that display X-like properties (see Usrey et al., 1999). It also should be stressed that, although we found no evidence for “parallel processing” of visual infor-

F1 response is zero, along with small F2 responses are indicative of linear spatial summation; almost all mouse dLGN cells displayed this type of behavior. D, Temporal frequency (TF) tuning, assessed with drifting sinusoidal gratings. Like this cell, most mouse neurons have bandpass TF tuning curves and prefer TFs of about 4 Hz. E, Contrast response properties, also assessed using drifting sinusoidal grating stimuli. Mouse neurons, like this one, showed sigmoidal increases in response amplitude with increasing stimulus contrast, and usually had half-maximal responses (c50) at around 30%. Plots in different panels reflect the responses of different mouse dLGN cells. (Modified from Grubb and Thompson, 2003.)

mation in the mouse dLGN cells we recorded, these data were based on the approximately 60% of cells that had clear center-surround RFs. The remaining 40% of cells, perhaps with more complex RFs, may then relay rather different, parallel information.

Retinogeniculate Inputs and Temporal Information

The processing of temporal information across the retinogeniculate synapse has been particularly well described in the mouse. RGCs are glutamatergic, and the signal they transmit to TCs is received exclusively (Chen and Regehr, 2000; Reichova and Sherman, 2004) by two types of ionotropic glutamate receptor with very different temporal properties: AMPA receptors (AMPARs), which display very rapid kinetics, and NMDA receptors (NMDARs), which operate on a far slower time scale (e.g., Chen and Regehr, 2000, 2003). Additional important differences, apart from the NMDAR’s famous voltage dependency, are that AMPARs desensitize rapidly on glutamatergic stimulation, while NMDARs, with their higher affinity for the neurotransmitter, can saturate under repetitive stimulation. Both of these

Figure 18.4 Cross-species comparison of dLGN visual response properties. A, In the spatial domain, mouse RF center diameters are by far the largest of any mammalian species studied, conferring low spatial acuity on the visual system as a whole. B, In the temporal domain, however, mouse peak temporal frequency (TF) compares favorably with that of other mammals, aside from macaque. C, In terms of contrast gain, cells in the mouse dLGN tend to fall midway between the performance of parvocellular (P) and magnocellular (M) primate dLGN neurons. For the sake of simplicity, only

phenomena lead to reduced responses at high RGC firing frequencies (Chen and Regehr, 2003). However, the slow nature of NMDAR responses can counteract the effects of saturation, allowing high-frequency firing to lead to summation of NMDA currents, whereas similar firing reduces the contribution made by rapid-kinetic AMPARs (Blitz and Regehr, 2003, see figure 18.2). The upshot is that AMPARs relay single RGC spikes more precisely, but NMDARs are required to allow TC responses to high-frequency RGC activity. TCs can follow RGC action potential trains precisely (so long as they are in tonic mode), but the precise effect of a given RGC firing pattern on TC activity will depend on the relative numbers of AMPARs and NMDARs at the retinogeniculate synapse (Blitz and Regehr, 2003).

A prediction based on this information, and given the strong, unitary nature of the retinogeniculate connection,

selected species and cell classes are shown, and separate cell classes are shown for a given species only when they differ largely from each other. (Mouse data from Grubb and Thompson, 2003; all other RF diameter data from table 1 in Van Hooser et al., 2005. Squirrel data from Van Hooser et al., 2003; cat data from Mukherjee and Kaplan, 1995; TF owl monkey data from Xu et al., 2001; TF macaque data from Derrington and Lennie, 1984; contrast squirrel monkey and owl monkey data from Usrey and Reid, 2000; contrast macaque data from Levitt et al., 2001.)

would be that the visual temporal properties of mouse dLGN cells would depend both on the temporal properties of their driving RGC input and on the mixture of ionotropic glutamate receptors present at their synapses. We know that mouse dLGN cells respond best to sinusoidal grating stimuli with temporal frequencies of about 4 Hz (Grubb and Thompson, 2003), but unfortunately, we have very little information about the temporal visual response properties of mouse RGCs. Similarly, since the responses of RGCs generally increase monotonically with increases in visual stimulus contrast (e.g., Kaplan and Shapley, 1986), one might expect the contrast response characteristics of mouse dLGN cells to also depend on RGC tuning and AMPARNMDAR distributions. Higher NMDAR ratios could be needed to be able to signal increases in the highest stimulus contrasts, reflected in higher RGC firing rates, but higher

AMPA ratios might allow changes in lower contrast to be reflected more faithfully in TC firing patterns. Again, although the contrast response properties are well described for mouse dLGN neurons (mean gain ca. 0.5 spikes/s/%, mean c50 ca. 33%; Grubb and Thompson, 2003), we lack the appropriate RGC data for comparison. Perhaps if future studies reveal large differences between RGCs and TCs in these phenomena, AMPAR-NMDAR distributions could be the reason. Indeed, it could be extremely interesting to look at mice carrying kinetics-altering mutations in AMPAR/NMDAR subunits (e.g., Lu et al., 2006)—would this lead to geniculate abnormalities in temporal or contrast visual response properties? This would be one situation in which the mouse dLGN would be a perfect model to combine the strengths of both in vitro and in vivo electrophysiological approaches. Indeed, the strength of the mouse dLGN as such a model is clear from the quantitative data noted: although mouse dLGN performs extremely poorly in the spatial domain, its performance is rather good in the temporal and contrast domains (see figure 18.4). Preferred temporal frequency may be much lower (ca. 4 Hz) in mouse than in macaque (ca. 10–16 Hz; Derrington and Lennie, 1984), but the mouse compares favorably with the cat (ca. 5 Hz; Mukherjee and Kaplan, 1995) and other primates (owl monkey: ca. 2 Hz, Xu et al., 2001; galago: ca. 2–4 Hz, Norton et al., 1988) in this respect. In terms of contrast responses, mouse dLGN values fall between those reported for primate M and P dLGN cells (Levitt et al., 2001; Usrey and Reid, 2000). So, even though the mouse visual thalamus operates at very low spatial resolution, it still provides cortex with good temporal and contrast information.

Reshaping the retinal signal

The previous section showed that retinal input to the mouse dLGN determines many of its most important physiological features. In particular, mouse dLGN cells resemble their RGC inputs extremely closely in terms of visual spatial processing. However, although one role of the mouse dLGN is undoubtedly to relay information from retina to cortex, the huge number of non-RGC synapses impinging on TCs (e.g., Sherman, 2005) suggests that this is certainly not all the mouse dLGN is doing. In fact, there are numerous ways in which modulatory input to the geniculate can alter the visual information passing through the structure. This subtle modulation of the visual signal, compared with the drastic changes in visual RF structure as vision is processed through both retina and cortex, is a unique feature of the dLGN within the visual system (Sherman, 2005). One can even view the mouse dLGN’s main role as providing a subtle reshaping of the retinal signal based on its owner’s current state and environmental demands.

Presynaptic Modulation of Retinal Input Before visual information arrives postsynaptically in the dLGN, it can be controlled by the effects of modulating inputs onto RGC terminals. These effects have been well described in the mouse and appear to be inhibitory: GABA acting through metabotropic GABAb receptors decreases the otherwise strong release probability at retinogeniculate terminals, while serotonin, operating through 5-HT1 receptors, also inhibits transmitter release (Chen and Regehr, 2003). This latter effect has been shown specifically to improve the signal-to-noise ratio for high-frequency RGC inputs (Seeburg et al., 2004). Another opportunity for the mouse to lead our knowledge in this field: retina-specific genetic manipulations of either of these regulatory channels could prove extremely useful in determining the importance of such presynaptic modulation in visual or thalamic function.

Local Geniculate Inhibition As noted earlier, the dLGN is special in the mouse thalamus, but normal when compared with all primate thalamic nuclei, for containing a population of GABAergic interneurons (Arcelli et al., 1997). The effect of these cells can be seen following electrical stimulation of the optic tract in an in vitro preparation and depends on the particular circuitry of the cell being studied. “Locked” IPSCs (Blitz and Regehr, 2005) can be seen with very short and consistent latencies following glutamatergic EPSCs, and likely reflect feed-forward inhibition: a single RGC monosynaptically contacts both a TC and a local inhibitory neuron, with the latter then contacting the same TC (figure 18.5). This could occur in specialized synaptic structures known as triads, where the terminal of a RGC, the dendrite of a TC, and the dendrite of a geniculate interneuron are all closely opposed and synaptically interconnected (Rafols and Valverde, 1973; Sherman and Guillery, 2003). What is the role of this locked inhibition? Probably to sharpen a TC’s temporal response to retinal input, especially that carried through AMPARs (Blitz and Regehr, 2005). “Nonlocked” inhibition (Blitz and Regehr, 2005) is also seen after optic tract stimulation, occurring at variable latencies, and probably reflecting input to TCs from inhibitory cells that are not presynaptically connected to the same RGCs (see figure 18.5). The unlocked inhibition likely serves to increase surround inhibition within the dLGN. If inhibitory cells are driven by inputs from the region surrounding a TC’s RF center, and if they share same center type (ON/OFF) as that TC cell, their action would increase the contrast of visual information sent to cortex.

In fact, this is one transformation of the visual signal that is well characterized over the retinogeniculate pathway— surround inhibition is much stronger in dLGN cells than in the RGCs that feed them (Hubel and Wiesel, 1961; Usrey et al., 1999), and this difference depends on local geniculate inhibitory activity (Norton et al., 1989). It also appears to be

Figure 18.5 Reshaping the retinal signal in the mouse dLGN. A, Nonlocked and locked inhibition produced by RGC stimulation. Left plots show the amplitude of EPSCs (solid circles) and IPSCs (open circles) produced as the stimulation of RGC fibers in the optic tract becomes stronger. In both cases, the all-or-nothing increase in EPSC magnitude signifies a single-input retinogeniculate connection (see figure 18.2). In nonlocked inhibition (top), IPSCs increase gradually in strength. This is consistent with the recruitment of RGC fibers other than those that contacted the recorded cell; these fibers then project to inhibitory dLGN neurons that do contact the recorded neuron (right). In locked inhibition (bottom), the IPSCs also show an all-or-nothing jump in response amplitude with increased stimulation strength. This is consistent with the inhibition depending on activation of the same RGC fiber that provides excitation to the recorded cell (right). IN, interneuron. B, Switching TC response mode through modulatory neurotransmission. In control conditions (black trace), the response of a mouse dLGN cell is recorded in current clamp from a holding potential of −73 mV. A depolarizing current injection then results in an LTS accompanied by burst mode firing. Without any alteration in the holding current, application of muscarine then depolarizes the cell to −58 mV, and subsequent current injection leads to a train of spikes in tonic mode. (A, Modified from Blitz and Regehr, 2005. B, From Meuth et al., 2006a.)

the case in the mouse. Although strong surround inhibition was seen in almost all mouse dLGN cells studied (only 12 of 92 were low-pass for SF-varying stimuli; Grubb and Thompson, 2003), such effects were rarely seen in mouse RGCs (Stone and Pinto, 1993; but it should be noted that

the retinal recordings were carried out in vitro, while those from the dLGN were in vivo). Interestingly, if this is an effect of local inhibition in the mouse dLGN, it does not depend on the topographical organization of the nucleus. In mice lacking the β2 subunit of the nicotinic acetylcholine (ACh) receptor, the retinotopic organization of the dLGN is disrupted (Grubb et al., 2003), but RF surrounds are entirely normal (Grubb and Thompson, 2004). We hope that other genetic manipulations could offer the chance in the near future to study the effects of GABAergic inhibition on visual thalamic processing.

Modulating Intrinsic TC Physiology Other modulatory influences come from outside the dLGN and act on the intrinsic conductances of TCs. A huge number of glutamatergic feedback projections impinge on dLGN TCs from visual cortex, where the effects of each individual synapse are weak, with low release probability (Reichova and Sherman, 2004). However, in addition to the AMPARs and NMDARs that act at the glutamatergic retinogeniculate synapse, strong synergistic activation of inputs from mouse cortex can also activate metabotropic glutamate receptors (mGluRs), producing a slow depolarization of the TC membrane (Reichova and Sherman, 2004).

Many other extrinsic inputs to mouse dLGN TCs also act to produce slow changes in membrane potential. Studies in the mouse have shown that the easiest way to alter TC membrane voltage is probably through changes in the opening of Kleak and Ih channels—the two interact to largely determine resting membrane potential (Meuth et al., 2006b). Indeed, ACh acting through metabotropic muscarinic receptors (mAChRs) in the mouse dLGN produces a slow TC depolarization by closing Kleak channels (Meuth et al., 2006a). In contrast, activation of β-adrenergic receptors or GABAB receptors (from interneurons in the TRN, and maybe from local interneurons, too) have depolarizing and hyperpolarizing effects, respectively, on mouse TC membrane voltage through cAMP-controlled modulation of Ih (Frere and Luthi, 2004). It remains to be shown whether the multitude of other slow modulatory effects on TC membrane potential, as have been described in other species (see, e.g., McCormick and Bal, 1997), also occur in the mouse, where once again, the application of genetic interventions might prove crucial in explaining their underlying molecular mechanisms (e.g., Meuth et al., 2006a, 2006b).

The common and most important feature of all of these modulatory mechanisms is that their effects on TC membrane potential are slow. The importance of this becomes obvious when considering the properties of IT. Both inactivation and deinactivation of IT are slow processes. It takes approximately 100 ms for IT to inactivate at depolarized potentials, and approximately 100 ms for it to become deinactivated at hyperpolarized voltages ( Jahnsen and Llinas,

1984). The kinds of depolarization or hyperpolarization produced by ionotropic receptors or by depolarizing action potentials, then, although having huge effects on mouse dLGN response properties, will not affect TC response mode. In contrast, the slow hyperpolarizations and depolarizations produced by the mouse dLGN’s modulatory input are ideal for regulating IT, and thus the burst versus tonic response mode of the nucleus’s relay neurons. Indeed, to give just one example from the mouse, ACh shifts TCs from burst (IT-activated) to tonic (IT-inactivated) mode through the slow depolarizing influence of closed Kleak channels brought about by mAChR activation (Meuth et al., 2006a; see figure 18.5). The final effect of dLGN modulation, then, is often to switch TC response mode from burst to tonic mode, or vice versa.

This effect is extremely important in transitions from sleeping to waking states. Bursting predominates in the former, but with the transition from slow-wave sleep to wakefulness, modulatory inputs from the brainstem produce a slow depolarization of TCs that inactivates IT, decreasing burst firing and promoting the tonic responses that dominate the waking state (McCarley et al., 1983; McCormick and Bal, 1997; Llinas and Steriade, 2006). But could modulatory influences, and thus the firing mode of mouse TCs, also play a role in the way visual information is coded?

Burst and Tonic Mode Contributions to Visual Processing Because rhythmic bursting is most prominent during slow-wave sleep (McCarley et al., 1983; Weyand et al., 2001), and because during sleep the responsiveness of dLGN neurons to RF stimulation is decreased (Livingstone and Hubel, 1981), it might appear that burst firing in TCs is unimportant as a carrier of visual information. However, experiments carried out mainly in the cat have uncovered the possibility that burst and tonic responses carry specific types of visual information. Intracellular recordings in cat dLGN showed that burst responses driven by IT activation can occur during visual stimulation (Lu et al., 1992), and also showed that burst spikes could be accurately identified purely on the basis of temporal patterns in spike firing. The latter finding has allowed less technically demanding extracellular recordings to confirm that bursting, like tonic firing, can occur in the dLGN in awake behaving animals (Guido and Weyand, 1995; Weyand et al., 2001), and to show in anesthetized animals that the visual response properties of the two firing modes can be very different. Among other differences, burst spikes in the cat follow temporal changes in visual stimuli less faithfully (Guido et al., 1992; Lu et al., 1992), offer better stimulus detection (Guido et al., 1995), are more tightly tuned for TF (Mukherjee and Kaplan, 1995), and have more reliable timing (Guido and Sherman, 1998) than their tonic counterparts. Changes in firing mode in mouse thalamic relay cells brought about

by modulatory influences on IT might therefore affect the information relayed by those cells to cortex.

We recently explored whether burst and tonic firing might also be able to encode different forms of visual information in the mouse dLGN (Grubb and Thompson, 2005). As expected given the cross-species homology of IT and firing modes described in vitro, we found that the criteria developed to identify burst spikes in cat dLGN also identified distinct groups of spikes in the mouse—the criteria successfully classified preexisting temporal patterns of neuronal firing (figure 18.6). Comparing burst and tonic spikes classified in this way across a range of quantitative visual response parameters revealed that both firing modes were identical in terms of the spatial information they could encode. RF characteristics, the linearity of spatial summation, and SF tuning were all identical in burst versus tonic firing (Grubb and Thompson, 2005). Indeed, RF center and surround sizes were also shown to be identical in cat dLGN (Alitto et al., 2005). However, the two firing modes in the mouse dLGN did differ significantly in the temporal domain. Just as in the cat, bursts are more phase advanced, more rectified, have sharper TF tuning, and prefer lower TFs (Grubb and Thompson, 2005). We also demonstrated that contrastresponse curves are more steplike for burst responses, a feature that may arise, at least in part, from the all-or-none nature of the IT-driven LTS (see Zhan et al., 1999). Finally, just as in the cat, we found in the mouse dLGN that higher levels of bursting are associated with better stimulus detection (see figure 18.6), and that burst spikes offer more reliable information about stimulus onset (Grubb and Thompson, 2005).

This all suggests not only that the cat and mouse dLGN may be extremely functionally similar but also the possibility that burst and tonic firing in the mouse dLGN could signal (subtly) different types of visual information. Why might this be important? It may not be. It is a distinct possibility that the response properties of burst versus tonic firing are epiphenomena arising from mechanisms that evolved to switch cortex on and off during sleeping and waking states (e.g., Llinas and Steriade, 2006). However, another hypothesis of thalamic function (e.g., Sherman, 2001) suggests that bursting may function as a wake-up call for cortex: occurring at the start of a stimulus, and possessing good capability for stimulus detection, bursts could signal to cortex that that stimulus has occurred, and that it started a certain time ago. Bursts could also send a great deal of information about the stimulus (especially in the spatial domain), information that cortex can start to process before more detailed tonic spikedriven information arrives. It remains to be shown whether burst firing during stimulus presentation produces better behavioral detection of that stimulus (see Ruiz et al., 2006). However, the observation that the burst-tonic distinction occurs in the mouse dLGN and that it appears to have many