Warren et al. (2010) revisited singing-driven gene regulation in area X and found 474 known genes (represented by 807 probes) that were regulated over the course of 0.5–7 hr of singing. Three hundred of these genes were in our network, with subsets enriched in the three song modules (blue: 71 genes, with, e.g., SHC3, SMEK2, and NTRK2 having the highest GS.motifs.X, p < 4e-28; orange: 17 genes, e.g., CSRNP3, SCN3B,

http://www.selleckchem.com/erk.html and PLCB1, p < 3e-6; dark green: 38 genes, e.g., BSDC1, VLDLR, and RORA, p < 5e-5; Fisher's exact test; Table S2) and in one other module (yellow: 104 genes, p < 5e-7; Table S2). Compared to the rest of the network, probes for all 300 genes had greater expression increases (p = 1.9e-12, Kruskal-Wallis test; 882 probes total), higher GS.motifs.X (p = 7.8e-11), and higher GS.singing.X (p = 2.7e-11; Table S2). These genes were also more interconnected in their respective modules throughout the network (kIN.X, p = 4.2e-4), especially in the blue song GW-572016 nmr module (p = 3.8e-14). A separate aspect of the study revealed enrichment for the functional annotation term “ion channel activity” in 49 genes posited to have undergone positive selection in zebra finches, which are also suppressed in the auditory forebrain during song perception. Of these, 42/49 were in our network (114 probes; Table S2),

with six in the orange song module (p < 3.3e-4, Fisher's exact test). One of the ion channel genes, TRPV1 (dark green/salmon modules), was highly connected and strongly suppressed by singing in our data, and thus selected for validation in area X in vivo (see below and Table S2). We previously showed that FoxP2 mRNA and protein are lower in area X following 2 hr of undirected singing compared to nonsinging, with the magnitude of downregulation correlated to singing (Miller et al., 2008, Teramitsu and White, 2006 and Teramitsu et al., 2010). This finding was reproduced here; expression levels for all 12 FOXP2 probes in the network were negatively correlated with the number of motifs sung ( Figure S5). Although our study

used an indirect approach, i.e., a behavioral paradigm in which the birds’ natural singing behavior significantly alters FoxP2 levels within area X ( Miller until et al., 2008, Teramitsu and White, 2006 and Teramitsu et al., 2010), we predicted that this paradigm coupled with WGCNA would reveal FoxP2 transcriptional targets in area X singing-related modules. To test this, we screened the network for direct FOXP2 targets previously identified by three studies. Of 175 targets found in human fetal basal ganglia ( Spiteri et al., 2007), 56 were in our network (149 probes total; Table S2). These had relatively high MM in the orange song module (p = 0.05, Kruskal-Wallis; Table S2), which contained genes that were downregulated with continued singing, including 9/12 probes for FOXP2. Of 302 targets found by a second study in SY5Y cells ( Vernes et al.

If a neuron has a preferred direction close to target direction, contributes only to the numerator of a vector-averaging decoder, and lacks neuron-neuron correlations with neurons that contribute to the denominator, then it will contribute positively to the estimate of eye velocity for pursuit and its MT-pursuit correlations should INCB018424 purchase be positive. This is the situation we created in the decoder that does the best job of predicting the MT-pursuit correlations in our data. It is tempting to draw parallels between the initiation of pursuit eye movements

and motion perception. Their similar direction and speed discrimination thresholds suggest that they share the correlated noise source represented by the population response in MT (Osborne et al., 2005). MT-pursuit correlations are related to the finding that the activity of many individual MT neurons is weakly Dinaciclib research buy predictive of the perceptual decision a monkey will make (Britten et al., 1996) even when the stimulus lacks correlated motion. The limited noise reduction between MT neurons and pursuit eye movements may be related to the similarity between neurometric thresholds of MT neurons and psychometric thresholds of monkeys for direction discriminations near threshold (Newsome et al., 1989) and to the temporally causal correlation between MT firing and perceptual decisions

(Smith et al., 2011). Both pursuit and perception operate as Phosphoprotein phosphatase if only a handful of MT neurons are contributing signals for the behavior, even though it seems likely that tens of thousands of correlated neurons are involved (Shadlen et al., 1996). At the same time, pursuit and perception behave differently in a number of

ways. Pursuit attempts to estimate target motion and program a pursuit eye movement that matches any arbitrary estimate of motion (Lisberger and Westbrook, 1985 and Osborne et al., 2005), while perception is normally trying to discriminate among two or a few possibilities. Further, pursuit must estimate target parameters quickly, on the basis of only a few spikes in each MT neuron (Osborne et al., 2004). We think that pursuit’s estimates of sensory parameters are the result of a machine-like neural circuit that draws from all MT neurons. Perception may be able to use optimal decoding schemes (Jazayeri and Movshon, 2006) or take the time needed to select the neurons that provide the most important signals. For example, the most responsive neurons appear to contribute most strongly for coarse discriminations, while fine discriminations seem to depend on neurons that are stimulated on the flanks of their tuning curves (Britten et al., 1996, Cohen and Newsome, 2009, Jazayeri and Movshon, 2006 and Purushothaman and Bradley, 2005). These differences would not prevent pursuit and perception from having similar noise levels (Osborne et al., 2005).

, 1998 and Yang et al., 2009). Similar approaches using morpholinos in Xenopus and zebrafish embryos have also been reported (for example, Wilson and Key, 2006, Kee et al., 2008 and Rikin et al., 2010). An effective artificial miRNA against Shh

has been described (miShh; Das et al., 2006), and we have shown that, as expected, it induces both pre- and postcrossing axon guidance errors when expressed in the floorplate at HH17 or earlier (Wilson and Stoeckli, 2011). Here, we coelectroporated Math1-EGFPF-mi7GPC1 and Hox-EBFP2-miShh constructs at low concentrations I-BET-762 mw to reduce GPC1 in dI1 neurons and Shh in the floorplate ( Figures 3A and 3A′). Under these conditions, the single knockdown of each gene did not significantly affect axon guidance compared to control embryos expressing only mi1Luc. However, the concomitant knockdown of axonally expressed GPC1 and floorplate-derived Shh led to increased defects in the guidance of postcrossing axons ( Figures 3B–3F; Table S2). Interestingly, we did not see any increase in ipsilateral errors ( Table S2), suggesting that GPC1 does not influence the attractive activity of Shh in precrossing axons. This finding is in line with results from a separate

series of experiments in which we interfered with GPC1 expression at earlier stages (HH12–HH14; at least 15 hr before the commissural neurons begin to project axons) and saw no additional effects on precrossing axons ( Table S3). In particular, ABT 199 we did not find axons that failed to reach the floorplate, as would be expected if GPC1 and Shh would cooperate in the attraction of precrossing axons. Taken together, our results suggest that GPC1 and Shh collaborate specifically during postcrossing commissural axon guidance. To strengthen this interpretation, we also performed experiments in which we knocked down Shh together with Contactin2 (Cntn2), a gene that acts in

a different pathway to regulate midline crossing. We have previously shown that axonally expressed Cntn2 interacts with midline-derived NrCAM to make axons enter the floorplate (Stoeckli and Landmesser, 1995 and Wilson and Stoeckli, 2011). In postcrossing axons, Cntn2 interacts with NgCAM to regulate axon fasciculation (Stoeckli and Landmesser, 1995). In our Astemizole combinatorial knockdown experiments, the simultaneous knockdown of genes involved in parallel pathways should not cause a significant aggravation of the single gene manipulations. In line with this reasoning, we saw no exacerbation of either precrossing or postcrossing axon guidance phenotypes after combinatorial knockdown of Shh and Cntn2 (Figure 3F; Table S2). These findings strongly support our conclusion that GPC1 and Shh act in the same molecular pathway to regulate postcrossing commissural axon guidance. Next, we confirmed that GPC1 can directly bind Shh by performing coimmunoprecipitations.

g., Logothetis et al., 1995; Quiroga et al., 2005), and spike outputs from single neurons may influence the motor act of whisking in rodents (Brecht et al., 2004; Figure 1). However, individual neurons in the cortex are densely interconnected, both locally and distally, with a disparate population of other neuronal subtypes. In addition, single-modality sensory objects have many features that need to be coded together, and motor

outputs are often extremely complex. It is also rare for just one neuron to be activated by a single ATM Kinase Inhibitor in vivo stimulus or stimulus property (see Braitenberg, 1978; Abeles, 1988; Duret et al., 2006) but far more common that neurons may respond to multiple events in a sensory task (Vaadia et al., 1995). In addition, many sensory

inputs present multimodally and thus require the activation of numerous, spatially separate cortical regions (Singer, 2010). Thus, despite demonstrations of a clear role for individual neurons, Tofacitinib mouse the evidence for multiple neuronal involvement in sensory processing and motor activity has led to the suggestion that population coding is “inevitable” (Sakurai, 1998). If the output of a single neuron alone is rarely, if ever, sufficient to generate a useful representation of sensory input or motor output, then how many neurons are needed? In studies focusing on synaptic inputs to cerebellar granule cells during vestibular stimulation a highly precise relationship between individual neuron input and the vector of associated movement was seen ( Arenz et al., 2008). These authors estimated that as few as 100 synapses were needed to provide a resolution of sensory input approaching psychophysical limits ( Figure 2). The authors’ own caveat to this work is that the cerebellar granule cell used in this study is a simple neuron with only a few, well-defined inputs. More complex cortical neurons with large dendritic arbors may require the integration of far more inputs. However, see more using precisely targeted photostimulation of such complex neurons in superficial somatosensory cortex in

mice a similar magnitude of neuronal involvement correlated with a measure of psychophysical salience ( Huber et al., 2008; Figure 2). Following training, a correct behavioral response could be detected in mice with single action potentials being generated in as few as 300 neurons. This size of active population fell even further if individual neurons were stimulated to generate short trains of multiple action potentials (see below). From an anatomical perspective, assuming interconnectivity is required between cofunctioning neurons, evidence points to neuronal populations being highly distributed entities. Despite the tens of thousands of synapses on individual cortical principal cells, very few come from local excitatory neurons. Estimates for connectivity rates in pairs of principal cells within cortical regions range from ca. 1:25 to 1:400 (Deuchars and Thomson, 1996; Andersen, 1995).

A classic example is navigation through mazes (Tolman, 1938, Hull, 1932 and Olton and Samuelson, 1976). Recordings from the rodent hippocampus and entorhinal cortex have led to important discoveries about the neural encoding of navigation and the representation of space (McNaughton et al., 2006 and Moser et al., 2008). Navigation is composed of a sequence of individual orienting motions, but in contrast to rodent studies of spatial navigation, the neural control of individual orienting motions has been studied most thoroughly in primates, specifically with regard to the control of gaze by the frontal and supplementary eye fields (FEF and SEF) (Schall and

Thompson, 1999 and Schiller and Tehovnik, 2005). As a result of being separated by both different model species and by different behavioral paradigms, literature for the navigation system and literature learn more for the orienting systems have remained far apart, making few references www.selleckchem.com/products/ulixertinib-bvd-523-vrt752271.html to each other (but see Arbib, 1997, Corwin and Reep, 1998 and Kargo et al., 2007). Yet the two systems must necessarily interact (Whitlock et al., 2008). As part of bridging the gap between these two fields of research, we took a classic primate behavioral paradigm, memory-guided orienting (Gage et al., 2010 and Funahashi et al., 1991), which is known to be FEF-dependent

(Bruce and Goldberg, 1985 and Bruce et al., 1985), and adapted it to rats. Then, in rats performing the task, we studied a rat cortical area that has long been suggested as homologous to the primate FEF. The area we studied appears in the literature under a large variety of names. These include M2 (Paxinos and Watson, 2004), anteromedial cortex (Sinnamon and Galer, 1984), dorsomedial prefrontal cortex (Cowey and Bozek, 1974), medial precentral cortex (Leichnetz et al., 1987), Fr2 (Zilles, 1985), medial agranular cortex (Donoghue and Wise, 1982 and Neafsey et al., 1986), primary whisker motor cortex (Brecht et al., 2004), and rat frontal eye fields (Neafsey

et al., 1986 and Guandalini, 1998). A theme common to many studies of this area, and shared with the primate FEF, is a role in guiding orienting movements. We targeted a particular point first at the center of the areas investigated in the studies cited above (+2 AP, ±1.3 ML mm from Bregma), and refer to the cortex around this point as the frontal orienting field (FOF). The homology between rat FOF and primate FEF was first proposed four decades ago by C.M. Leonard (1969), based on the anatomical finding that the FOF, like the FEF, receives projections from the mediodorsal nucleus of the thalamus (Reep et al., 1984), and projects to the superior colliculus (SC) (Reep et al., 1987). Later, Stuesse and Newman (1990) found that the rat FOF also projects to other oculomotor centers in the rat’s brainstem, in a pattern that mimics the oculomotor brainstem projections of the primate FEF.

The animals were further implanted with a head stage to record extracellular potentials in the area identified as vM1 cortex based on mapping studies (see Figure S1, available online). These signals were subsequently sorted into single units, as verified through the consistency of the extracellular spike waveform and the presence of relative and absolute refractory periods in the spike train (Figures 1A–1D). In addition, we required the recording of at least 100 whisks for each unit to be accepted for further analysis. Given these constraints, our results are based on 95 single units across 11 rats. In ancillary studies with a lesion to the infraorbital branch

of the trigeminal nerve (IoN), an additional 74 single units across seven this website animals were obtained. Rhythmic exploratory whisking behavior consists of extended bouts of contiguous whisk cycles (Carvell and Simons, 1995). Qualitatively, the range of motion and the average position of the vibrissae tend to be similar for adjacent whisk cycles, consistent with past reports (Berg and Kleinfeld, 2003a and Hill et al., 2008), and thus vary on a

timescale that is much slower than that of the 0.1 s whisk cycle (Figure 1C). In addition, the large vibrissae tend to move in unison during exploratory whisking (O’Connor et al., 2010a and Welker, 1964), implying that a single set of control signals is sufficient to uniformly drive the vibrissae. We examined the latter issue in detail Dorsomorphin by tracking the motion among sets of vibrissae that spanned rows and arcs (Figure 1E). For the example of four vibrissae that span two rows and five arcs, we find a high degree of linear correlation between all vibrissae, TCL as quantified by the first mode of the singular value decomposition which accounts for 0.95 of the variability in the motion across all vibrissae (cf. colored and gray traces in Figure 1F) ((4) and (5)). In general, we observe that correlations in the motion about the mean position exceeded 0.90 for vibrissae within or across rows (Figure S2). A minimal analysis

is to test if both the slow and fast timescales of the vibrissa trajectory are coded linearly. We thus calculated the transfer function, H˜(f) (Equation 6), as a function of frequency, f, between unit spike trains and vibrissa position using epochs that contained whisking and nonwhisking behavior. The transfer function defines the linear relationship between the position of the vibrissae and a measured spike train. In practice, relatively few units tracked the angle of the vibrissae on a cycle-by-cycle basis. A particularly illustrative example of such data is shown in Figure 2A, together with the predicted whisking trajectory that was calculated by convolving the measured spike train with the transfer function (Figures 2A and 2B). The predicted trajectory captures the phase of the motion rather well, but fails to capture the envelope of the motion.

The short latencies of electrically evoked responses in the neonatal PL were consistent with the conduction time of hippocampal-prefrontal pathway (Tierney et al., 2004). However, neither the stimulation experiments nor the Granger analysis can reliably decide whether only such monosynaptic pathways drive the information from the Hipp to

PFC or whether third areas, like the EC or thalamus (Steriade and McCarley, 1990 and Vertes, 2006), are equally involved. Analysis of the spike-timing relationship between individual cell pairs in the two areas provided better understanding of the early communication between check details the PFC and Hipp. Multitetrode recordings in neonatal rodents revealed that the prefrontal firing may be driven at delays corresponding to monosynaptic projections by the hippocampal discharge. Such interpretation must, however, be tempered by the caveats of the cross-covariance analysis when applied to neonatal data. The low firing rate of prefrontal neurons not only dramatically reduced

find more the number of cell pairs suitable for the analysis, but also facilitated the detection of spurious cross-covariances (Siapas et al., 2005). The spike-timing interactions in prejuvenile prefrontal-hippocampal networks are supportive for the conclusions of the Granger analysis. The presence of prefrontal neurons firing shortly before or after the hippocampal cells argues for mutually interacting PFC and Hipp. Whether the directional change between neonatal and prejuvenile development is related to the strong network refinement and pruning during adolescence remains to be elucidated. Previous studies have shown that bursts of oscillatory activity are present in the neonatal primary sensory cortices (Khazipov et al., 2004, Hanganu et al., 2006 and Yang et al., 2009), where they may act as a template facilitating

the refinement of cortical maps (barrels, ocular dominance columns) (Dupont et al., 2006 and Yang et al., 2009). The intrinsic properties, spatial and temporal many organization as well as most likely the underlying mechanisms and the function distinguish the prefrontal oscillatory bursts from those recorded in the S1 or V1 (Hanganu-Opatz, 2010). Differences were noted also between the prefrontal areas Cg and PL. We propose that one of the factors leading to these differences is the area-specific impact of hippocampal drive. Early generated hippocampal theta bursts drive the prelimbic network by timing the gamma phase-locked neuronal firing within local networks and modulate to a lesser extent the cingulate activity. This different impact of hippocampal drive on the Cg versus PL is present also at adulthood, the cingulate activity being able to emerge independently of the Hipp (Leung and Borst, 1987).

Statistical analysis was performed using the SAS System for Windows (SAS Institute, Cary, NC, USA). Statistical significance was determined by Tukey’s test in Section 2.2.1, and by Dunnett’s test in Section 2.2.2. A dose of 2 μg/kg calcitriol or 0.2 μg/kg eldecalcitol administered daily by oral gavage for 14 days significantly increased BIBW2992 serum calcium and urinary calcium excretion compared with vehicle administration in WT mice. However, neither eldecalcitol nor calcitriol affected serum or urinary calcium in the VDRKO mice (Fig. 1A and

B). Calcitriol and eldecalcitol significantly increased the expression of renal TRPV5 and calbindin-D28k mRNA and the expression of intestinal TRPV6 and calbindin-D9k mRNA in the WT mice. On the other hand, the expression of these genes in the VDRKO mice was not altered by the treatment (Fig. 1C–F). These results indicate that the calcemic actions of calcitriol and eldecalcitol are mediated by VDR. Eldecalcitol (0.025, 0.05, 0.1, 0.25, and 0.5 μg/kg) or calcitriol (0.25, 0.5, 1, 2.5, and 5 μg/kg) administered daily by oral gavage for 14 days dose-dependently increased the blood concentration of each compound. The blood concentration of each compound correlated well with the administered dosage (eldecalcitol: y (pmol/L) = 29,834x (μg/kg) + 646.3, R2 = 0.996; calcitriol: y (pmol/L) = 681.81x (μg/kg) + 402.1, R2 = 0.971) ( Fig. 2A and B). This result indicates that in order to

reach the same concentration in the blood, the amount of eldecalcitol Etomidate required is approximately 1/40 that of calcitriol. In the eldecalcitol-treated rats, serum concentration of calcitriol dose-dependently decreased and selleck inhibitor fell to below the limit of detection at 0.1 μg/kg (

Fig. 2C). Treatment with eldecalcitol and calcitriol significantly reduced renal CYP27B1 gene expression and dose-dependently increased renal CYP24A1 gene expression ( Fig. 2D and E). These results suggest that the administration of eldecalcitol and calcitriol reduces endogenous production of calcitriol and stimulates degradation of calcitriol in the kidneys. Blood concentrations of eldecalcitol and calcitriol correlated with urinary phosphorus excretion. Serum phosphorus slightly decreased along with the increase in eldecalcitol concentration in blood, whereas calcitriol concentration did not alter serum phosphorus (Fig. 3A and B). Serum calcium was significantly elevated at higher blood concentrations of eldecalcitol (≥7520 pmol/L) and calcitriol (≥1170 pmol/L) (Fig. 3C). Urinary calcium excretion correlated with blood concentrations of calcitriol and eldecalcitol (Fig. 3D). Serum FGF-23 increased at 15,800 pmol/L of eldecalcitol and at ≥2480 pmol/L of calcitriol in blood (Fig. 3E). High concentrations of eldecalcitol in the blood (≥7520 pmol/L) suppressed plasma PTH concentration, whereas plasma PTH concentration was reduced from low blood calcitriol concentrations (≥590 pmol/L) (Fig. 3F).

A challenge for neuroscientists is to understand not only how safety memories are encoded alongside fear memories in the brain, but also how these different

memories come to be regulated by time and context. Given that considerable progress has been made in elucidating the neural substrates of extinction, several investigators have examined whether extinction learning can be facilitated. Some of the early work in this arena focused on the FDA-approved drug, D-cycloserine (DCS), which is an allosteric modulator of the NMDA receptor that facilitates agonist binding therefore increasing NMDA receptor function. It has been reported that either systemic or intra-amygdaloid administration of DCS facilitates extinction learning SNS-032 ic50 (Ledgerwood et al., 2003, Ledgerwood et al., 2005 and Walker et al., 2002). Interestingly, Fulvestrant there have been reports that the extinction memory acquired under DCS is less likely to exhibit recovery (e.g., reinstatement) (Ledgerwood et al., 2004), although this outcome has not been universally reported (Bouton et al., 2008 and Woods and Bouton, 2006). Moreover, administering DCS prior to extinction in rats appears to promote the reversal of some of the

synaptic changes in the lateral amygdala that accompany fear conditioning (Mao et al., 2008). Preliminary work using DCS as a pharmacological adjunct to exposure therapy has shown some promise (Davis et al., 2006). For example, administration of DCS along with controlled exposure therapy improved outcomes for patients with fear of heights (Ressler et al., 2004) and social anxiety disorder (Guastella et al., 2008), although it did not improve therapeutic outcomes for spider phobics (Guastella et al., 2007a) or effect the extinction of fear conditioning (Guastella et al., 2007b). to Another compound that

has been reported to enhance extinction learning is yohimbine, an alpha2-adrenergic agonist that has been used in humans to treat erectile dysfunction. Cain and colleagues reported that systemic yohimbine administration prior to extinction training in mice increased the long-term retention of extinction the following day (Cain et al., 2004). However, the effects of yohimbine on extinction are quite variable, in some cases even impairing extinction learning (Holmes and Quirk, 2010). Nonetheless, a recent report in humans suggests that yohimbine administration enhances the efficacy of exposure therapy in claustrophobic patients (Powers et al., 2009). The role for the adrenergic system in extinction learning is likely to be quite complex; however, insofar as prazosin, an alpha1-adrenoceptor antagonist, has recently been reported to impair fear extinction in mice after systemic (Bernardi and Lattal, 2010 and Do-Monte et al., 2010) and intra-vmPFC administration (Do-Monte et al., 2010).

This inhibition was absent from WT GluA2 in equivalent conditions (Figure 6D). The lack of inhibition at high glutamate concentration was not due to chelation of zinc into zinc-glutamate complexes because we still observed robust inhibition when the patches were washed with 1 μM zinc, 500 μM L-glutamate, and 9,500 μM D-glutamate, which barely activates the receptor but should chelate zinc equally well. The glutamate dependence of trapping was similar to that of the A665C mutant, with a maximum extent of trapping at 348 μM glutamate (Figure 6E). Thus, the zinc-binding site created by the HHH mutant, which was suggested by

the crosslinked crystal structure, selleck chemicals also traps a partially bound state and does so with more specificity than the A665C mutant. Structural modeling of the HHH mutant built using our LBD tetramer structure, where only residues 436–440 and 455–457 (those flanking the HHH substitutions) were repositioned using energy minimization, shows that a zinc Selleckchem Forskolin ion can be cradled by the three histidines (Figure 6F). The repositioned residues all lie in loop regions, and the rmsd measured at the Cα atoms of these residues is only 0.75 Å. These observations constitute strong evidence that the crystallized CA conformation occurs in the full-length receptor when some, but

not all, of the ligand-binding sites are occupied by glutamate. Structural modeling was pursued to examine possible consequences of OA-to-CA

conformational transitions that occur in conjunction with LBD closure in subunits B and D on ion channel pore opening. First, the closure of subunits B and D in the crystal structure of the crosslinked LBD tetramer was modeled by superimposing the structure of a closed, glutamate-bound LBD (PDB ID 1FTJ; chain A) (Armstrong and Gouaux, 2000) at helices D and J in lobe 1. Next, the TMD from the full-length GluA2 crystal structure was allowed to relax energetically to accommodate the LBDs (Figure S7). In this model, the inner transmembrane helices (M3) are predicted to widen at the Linifanib (ABT-869) ion channel gate between subunits B and D by ∼11 Å, as measured between Cα atoms of T625. It should be noted that NMA was attempted with both the full-length GluA2 structure and the isolated TMD, but the ion channel gate could not be opened in either case, likely due to the tight network of residues around the gate. Over 80 crystal structures of the isolated GluA2 LBD have been reported to date (Pøhlsgaard et al., 2011). These structures, in concert with biochemical and biophysical experiments, and molecular simulation studies, have characterized the processes of ligand binding and domain closure, which are directly linked to receptor activation (Armstrong and Gouaux, 2000 and Dong and Zhou, 2011). Less is known, however, about the possible intersubunit conformational rearrangements in iGluR tetramers that could underlie ion channel gating.