Baclofen mildly reduces odor-evoked EPSCs for selectively tuned neurons, but strongly blocks the excitatory responses and

thus sharpens the tuning for INK1197 purchase broadly-tuned neurons. In contrast, baclofen exerts similarly effective blockade of odor-evoked inhibitory responses on all neurons irrespective of their excitatory tuning. Because all PNs appear to receive the same amount of excitation from LOT inputs, the authors suggest that intracortical excitations might contribute strongly to the odor-responses of broadly-tuned neurons but may have weak effects on selectively responsive neurons. Together, these studies (Franks et al., 2011 and Poo and Isaacson, 2011) offer several insights into our understanding of the circuit wiring scheme in the piriform cortex (Figure 1) and suggest important roles of recurrent intracortical connections in shaping cortical odor representation. Their results indicate that, although the chance of any pair of PNs forming synaptic connections is low, an individual PN might receive excitatory inputs from thousands of other PNs across a distance

of millimeters. In addition, PNs can activate local GABAergic neurons to generate global inhibition to maintain the balance of cortical activity. In response to an odorant, the bulbar input might activate a small subset of PNs, which then recruits a larger population of PNs and interneurons check details to generate a complex pattern of excitation and inhibition in the piriform. Some PNs may receive stronger intracortical excitation than others and thus exhibit broader olfactory tuning. Results from these two new studies suggest additional experiments to pin down the exact wiring pattern in the piriform cortex. Do all PNs

play equal roles in intracortical association? Morphologically identified PNs exhibit highly diverse olfactory tunings in terms of both excitation and inhibition in an awake mouse (Zhan and Luo, 2010). Both new studies indicate heterogeneity among PNs in receiving intracortical excitation. In vivo recordings show that broadly tuned neurons tend to Carnitine palmitoyltransferase II be more frequently activated by odor-elicited intracortical excitation (Poo and Isaacson, 2011). Consistently, individual PNs appear to receive quite variable amount of recurrent excitation in slice preparations (mean ± SD: 441 ± 334 pA; Franks et al., 2011). It remains unclear whether PNs uniformly extend long-range axonal terminals to synapse on thousands of other PNs, or whether some subtypes of PNs exert much stronger influence on other PNs. This may be tested by serially activating small subset of ChR2+ neurons with focal illumination and mapping area-specific effects on a group of ChR2− neurons. Interneurons represent another major class of cortical neurons. Although the interneurons are labeled as a single type of inhibitory neurons in the diagram (Figure 1), they can be divided into various subtypes based on their morphological, neurochemical, and functional features.

An important advance in the ability to identify and study rare variants comes from innovations in sequencing

technology. Today the protein-coding parts of a patient’s Selleckchem Entinostat genome (the “exome”) can be sequenced for well under $1,000, enabling exome-sequencing studies of hundreds of patients. As in the case of common variants, it is challenging is to distinguish the rare variants that contribute to a phenotype, from the background of other rare variation that is present in each genome. To reduce the background created by the hundreds of protein-altering variants in each genome, one common study design sequences father-mother-proband trios, then focuses on those protein-altering mutations in the proband that are de novo mutations, LY294002 order i.e., that were not inherited from either patent. The challenge in this analysis comes from the fact that protein-altering mutations unrelated to disease arise in the general population at an appreciable rate. Disease-predisposing variants are not immediately distinguishable from this background, except to the extent they recur in the same genes in different individuals with the disease under investigation. To date, the most convincing implication of individual genes has come from studies of congenital and child-onset disorders such as autism, intellectual

disability, and pervasive developmental delay. For autism, four large studies of father-mother-offspring trios collectively ascertained de novo mutations in more than one thousand autism patients (Sanders et al., 2012, O’Roak et al., 2012a, Neale et al., 2012 and Iossifov et al., 2012). Analysis of the trios from these studies, when considered jointly, identified CHD8 and SCN2A as genes harboring recurrent, disruptive mutations nearly in autistic patients. Deeper sequencing of 44 genes in another 2,446 patients also observed recurrent mutations

in DYRK1A, GRIN2B, TBR1, PTEN, and TBL1XR1 ( O’Roak et al., 2012b). Notably, studies of de novo mutations in children with severe intellectual disability identify mutations in some of these same genes ( Rauch et al., 2012 and de Ligt et al., 2012). De novo mutations may make a smaller contribution to teen or adult-onset disorders such as schizophrenia: studies have not yet found statistically convincing levels of recurrent mutations in individual genes, though one study reports a greater-than-chance rate of mutations in cortically expressed genes as a group ( Girard et al., 2011, Xu et al., 2012 and Gulsuner et al., 2013). The results of exome sequencing studies support models of significant polygenicity for autism and schizophrenia. Iossifov and colleagues estimate from the statistical distribution of disruptive mutations across genes that 350–400 autism susceptibility loci exist in the genome—an estimate broadly consistent with estimates from the distribution of de novo CNVs (Iossifov et al., 2012 and Sanders et al., 2011). Lim et al.

The conformation of

the Tyr57 side chain in the heterodimer assembly is also stabilized by van der Waals contacts with the Cys65-Cys316 disulfide bond in loop 3 of the interacting GluR6 protomer, and by a hydrogen bond between the main chain amide of Tyr57 and the hydroxyl group of Ser89 on α-helix C of the GluR6 protomer (Figure 3A). A hydrogen bond between GluR6 Lys62 in α-helix B and the main chain carbonyl of Cys315 in loop 3 of the KA2 protomer further stabilizes the heterodimer interface. On the 2-fold related side of the heterodimer assembly, the side chain of Phe58 at the base of α-helix B in the GluR6 subunit makes hydrophobic contacts with His89, Ile90 and the loop 3 Cys64-Cys315 disulfide bond of the KA2 protomer (Figure 3B), but as noted above cannot form a hydrogen bond contact with loop 3 of the KA2 subunit. Movie S1 shows details of these contacts. To test the importance of intersubunit interactions made by the GluR6 selleck products Phe58 and KA2 Tyr57 side chains, which occupy similar positions in the heterodimer and GluR6 Veliparib clinical trial homodimer assemblies, we made the GluR6Δ2 F58A and KA2 Y57A mutants and used sedimentation velocity experiments to measure changes in Kd for assembly of ATD homodimers and heterodimers. Strikingly, for SV runs at loading concentrations of 1.2 μM to 47 μM the c(s) peak distribution for the GluR6Δ2 F58A

ATD mutant was largely monomeric (Figure 3C). Analysis of weighted-average sedimentation coefficient isotherms (Figure 3F) yielded a Kd value for homodimer formation of 490 μM (95% confidence interval; 380 μM–650 μM), 2000-fold science higher than for GluR6Δ2. However, when mixed with the KA2

subunit ATD, the sedimentation profile for the GluR6Δ2 F58A mutant shifted to higher S values and showed the characteristic pattern for a reversible monomer-dimer system in rapid equilibrium (Figure 3D). Analysis of sw(S) isotherms gave a Kd for heterodimer formation of 0.109 μM (95% confidence interval; 0.096 μM–0.121 μM) 10-fold weaker than the value measured by SV for wild-type (Kd 11 nM). Likewise, SV analysis for a mixture of the GluR6Δ2 and KA2 Y57A mutant ATDs (Figure 3E) gave a similar Kd for heterodimer assembly of 0.14 μM (95% confidence interval; 0.11 μM–0.18 μM). However, when the aromatic side chains were mutated to alanine in both subunits (Figure 3F), the Kd for heterodimer assembly by the GluR6Δ2 F58A and KA2 Y57A mutant mix increased 150-fold to 1.63 μM (95% confidence interval; 1.57 μM–1.70 μM). The fact that the GluR6 Δ2 F58A mutant still forms high affinity heterodimers with KA2, even though its ability to assemble as homodimers is essentially abolished, suggests that while the interaction of Phe58 is very important for GluR6 homodimer formation, other regions, most probably the R2 domain, must make a substantial contribution to heterodimer formation with KA2.

, 2008). Based on the current work, we propose the following model for Plk2 function (Figure S7J): synaptic activity

induces expression of Plk2, which coordinately targets via PBD interactions to key regulators of Ras and Rap. Phosphorylation-dependent degradation of RasGRF1 (Ras activator), together with activation Selleckchem Screening Library of SynGAP (Ras inhibitor), dramatically reduces active Ras levels. Conversely, degradation of SPAR (Rap inhibitor) and activation of PDZGEF1 (Rap activator) work additively to stimulate Rap. The result of this mirror-image regulatory program is a profound shift in favor of Rap at the expense of Ras. Indeed, quantification under various conditions of synaptic activity (or Plk2 function) revealed that Ras and Rap can be bidirectionally regulated by Plk2 over ∼4000-fold difference in relative ratio of Rap to Ras activation state (Figure S7K). Thus, we propose that Plk2 abundance may act as a graded sensor coupling synaptic activity level to the fine-tuning of Ras and Rap balance over a wide dynamic range. In hippocampal neurons, silencing of Plk2 led to more and larger spines, consistent with a normal function for Plk2 in C646 promoting spine shrinkage and loss (Pak and Sheng, 2003). These effects were also observed in hippocampus of DN-Plk2 mice. Importantly,

PTX-mediated Thiamine-diphosphate kinase reduction of spine density and head width were abolished by blocking Plk2 activity using multiple independent methods. Therefore, Plk2 is required for homeostatic downregulation of dendritic spines in response to chronic overactivity. Individual knockdown experiments

as well as a series of epistasis tests demonstrated that each identified GAP/GEF acted downstream of Plk2 in controlling different aspects of dendritic spines. RasGRF1 consistently increased spine density but also affected spine length and width in some assays. In contrast, PDZGEF1 selectively suppressed spine density. SynGAP reduced spine width, consistent with larger spines observed in SynGAP-deficient mice (Vazquez et al., 2004), while SPAR strongly increased head size along with exerting modest effects on spine density. These results suggest that, despite some overlap in function, each regulator fulfills a primary responsibility in homeostatic spine regulation, with RasGRF1 antagonistic to PDZGEF1 in controlling spine density and SPAR opposing SynGAP in spine size control (Figure S7L). These observations may explain the necessity of regulating both Ras and Rap signaling arms by Plk2. An alternative, but not mutually exclusive, possibility is that Plk2 actions on multiple GAPs/GEFs allow synergistic shifts in Ras and Rap balance.

One of the many ways neuromodulators influence synaptic transmission is by regulating release of neurotransmitters. Neuromodulators can initiate changes in release probability (Prelease) either http://www.selleckchem.com/products/Adriamycin.html by activating presynaptic receptors or by eliciting the liberation of retrograde signaling molecules from the postsynaptic membrane. Thus, modulation of Prelease by DA cannot simply be inferred based on presynaptic localization of DA receptors, nor can it be excluded in their absence. For the purposes of this

Review, we focus on electrophysiological studies in acute brain slices that clearly identify a presynaptic modulatory effect of DA either through analysis of tetrodotoxin (TTX)-resistant “miniature” excitatory or inhibitory postsynaptic currents (mEPSCs or mIPSCs), paired-pulse ratios, or evoked excitatory or inhibitory postsynaptic trans-isomer currents (EPSCs or IPSCs) when postsynaptic changes in neurotransmitter receptor composition have been excluded. DA acting through both D1 and

D2 receptor families has been implicated in heterosynaptic regulation of Prelease at glutamatergic, GABAergic, and cholinergic terminals ( Figure 3). Specifically, D2-like receptor activation decreases release of glutamate onto SPNs in dorsal and ventral striatum ( Bamford et al., 2004; Higley and Sabatini, 2010; Salgado et al., 2005; Wang et al., 2012). D2-like receptors also decrease Prelease of GABA

onto PFC pyramidal neurons ( Chiu et al., 2010; Seamans et al., 2001b; Xu and Yao, 2010), SPNs ( Delgado et al., 2000; Guzmán MTMR9 et al., 2003; Kohnomi et al., 2012; Taverna et al., 2005; Tecuapetla et al., 2009), and striatal interneurons ( Bracci et al., 2002; Centonze et al., 2003; Momiyama and Koga, 2001; Pisani et al., 2000). In addition, D2-like receptors depress release of acetylcholine (Ach) onto striatal cholinergic interneurons ( Pisani et al., 2000). D1-like receptor stimulation decreases release of glutamate onto L5 pyramidal cells in PFC ( Gao et al., 2001; Gao and Goldman-Rakic, 2003; Gonzalez-Islas and Hablitz, 2003; Seamans et al., 2001a) and SPNs in ventral striatum ( Harvey and Lacey, 1997; Nicola and Malenka, 1997; Pennartz et al., 1992; Wang et al., 2012) but not dorsal striatum ( Nicola and Malenka, 1998). Moreover, DA-mediated activation of D1-like receptors reduces GABA release onto cortical FS interneurons ( Towers and Hestrin, 2008), L2–L5 PFC pyramidal neurons ( Gao et al., 2003; Gonzalez-Islas and Hablitz, 2001), and SPNs in ventral striatum only ( Nicola and Malenka, 1997, 1998; Pennartz et al., 1992; Taverna et al., 2005). Thus, at synapses responsive to DA modulation, DA typically acts to decrease Prelease. There are, however, some notable exceptions to this simple view.

, 1978 and Neale and Cardon, 1992). This method can be used further to determine the magnitude of genetic and environmental covariance between phenotypes; in other words, it is possible to estimate the degree to which phenotypes share common genetic and/or environmental influences. These estimates refer to genetic and environmental correlations, respectively. Utilizing cortical surface reconstruction and spherical atlas mapping procedures developed by Dale and colleagues (Dale et al., 1999 and Fischl et al., www.selleckchem.com/products/Y-27632.html 1999b), we mapped each

individual’s surface reconstruction into atlas space. Maps of subject-specific areal expansion or contraction were then computed based on the local stretching or compression needed to map the subject’s surface into atlas space (Rimol et al., 2010a). Next, to examine

patterns of relative surface expansion, we divided the area measure at each location by the total surface area for each participant. The normalized data more directly correspond to the approach used in the animal literature (Bishop et al., 2000 and Paxinos and Watson, 2007) and make it possible to examine genetic influences on cortical regionalization after accounting for global effects. Although registration Selleck Crizotinib with atlas space is driven by cortical folding patterns, there is evidence that the folds are good predictors of the locations of functionally distinct regions (Fischl et al., 1999b). Genetic correlations among measures of relative areal expansion at different points on the cortical surface reflect shared genetic influences on surface area between cortical regions and for this reason were used to depict the genetic patterning of the cortex in this study. We used three complementary

approaches to explore the genetic patterns: (1) a hypothesis-driven, seed-based approach; (2) a “marching seed” approach; and (3) a hypothesis-free clustering approach. For the hypothesis-driven, seed-based approach, four seed points were placed at locations presumed homologous to the mouse “functional domains.” The V1 and S1 seed points were placed in the calcarine sulcus and postcentral gyrus, respectively. In order to adjust for the considerable expansion of human frontal and temporal cortices relative to those in the mouse, we also placed seed points in the rostral end of the frontal cortex (frontal pole) and temporal cortex (temporal pole). nearly We then created maps of genetic correlations between each of these seed regions and all other locations in the cortex. To address potential bias due to the selection of seed regions, and to assess the sensitivity of the patterns to the exact locations of the seed points, we next used a grid of regularly spaced marching seeds distributed across the entire lateral aspect of one cortical hemisphere. Furthermore, we performed additional fine-grained one-dimensional marching seed analyses to identify gradual or abrupt transitions of the genetic patterning (Cohen et al., 2008).

It will be important to determine the X-ray crystal structure of OBP49a alone and with various bitter check details compounds bound to establish whether there is a shared conformational change induced by the diverse bitter compounds that is distinct from the unliganded OBP,

similar to what has been shown for cVA pheromone and LUSH (Laughlin et al., 2008). Such a conformational shift could pinpoint domains that might interact with the taste receptor. Indeed, demonstrating bitter-dependent binding of OBP49a to the sweet receptor would also be important. Finally, it is fascinating that detection of bitter compounds with neurons located in S-type and I-type sensilla is not enough to deter flies from potential toxins mixed with sugars. OBP49a represents an independent bitter detection mechanism that Luminespib datasheet has evolved to override the activation of sugar neurons in the presence of bitter chemicals. It is likely that the sugar input into the CNS elicits a strong feeding signal that needs to be blocked when bitters are present to prevent feeding behavior, and this two-pronged mechanism prevents “the spoonful of sugar” from facilitating ingestion of potentially toxic bitter “medicines. “
“A central finding of human cognitive neuroscience is that specific regions of visual cortex respond preferentially to certain ecologically

important stimulus categories. For example, the fusiform face area (FFA) responds more strongly to faces than to nonface objects during fMRI, and the parahippocampal place area (PPA) responds more strongly to scenes (landscapes, cityscapes, rooms) than to nonscenes. Recent studies have identified a macaque homolog of the FFA, which has allowed the region to be explored using neurophysiological techniques. In contrast, only one previous study has identified a PPA homolog in macaques (Nasr et al., 2011), and no neurophysiological recordings have been enough made from this region. This lacuna

has now been filled by Kornblith et al. (2013), with potentially important consequences for our understanding of the neural basis of scene recognition and spatial cognition. Using a combination of neuroimaging (fMRI) and microstimulation, Kornblith et al. (2013) identify two scene regions in the macaque brain, which they label the lateral place patch (LPP) and the medial place patch (MPP). The LPP is located in the occipitotemporal sulcus just anterior to V4, while MPP is located in the medial parahippocampal gyrus. These locations are close to what would be expected given the location of the human PPA. LPP was identified using a standard fMRI localizer technique, directly analogous to the technique typically used to define the PPA in humans. Monkeys were scanned while fixating and passively viewing scenes, objects, and textures. The scenes were all indoor locations, some of which were familiar to the monkeys (e.g.

One may imagine the array of bipolar cell axon terminals as transmitting a cafeteria of stimulus properties, among which the ganglion cell chooses depending on the type of information that particular ganglion cell will finally transmit to central visual structures. This connectivity builds the initial foundation of the response selectivity that distinguishes functional types of ganglion cell: if the different retinal ganglion cells get selective inputs from differently responding bipolar Compound Library cells, they are right away

imbued with differing types of response to light themselves. Note that these connections are not limited to the one-to-one case—ganglion cells that stratify in several layers can take some of their properties from one type Crizotinib of bipolar cell, and other properties from a different one. A slightly tricky conceptual issue should be clarified here. There are two main influences upon the responses to light of bipolar cells. As just described, the first is their synaptic drive from the rod or cone photoreceptors, as expressed through the bipolar cells’ differing glutamate receptors and modified by their signaling proteins and ion channels. These features are intrinsic to the bipolar cells,

controlled by the set of proteins that each type of bipolar cell expresses. But the bipolar cells are also influenced by inputs from amacrine cells (Figure 5), and those effects are included in the bipolar cell’s “response to light” as well. Bipolar cells are short, fat neurons (Figure 1) and are electrotonically compact. Thus, a recording from the soma of the bipolar cell does not simply monitor a signal transmitted

second from dendrite to soma to axon of the bipolar cell, like watching a railway train pass a vantage point alongside its tracks. Instead, a soma recording monitors the effects of all of the bipolar cell’s inputs, including the signals that impinge on its axon terminals from amacrine cells (Bieda and Copenhagen, 2000; DeVries and Schwartz, 1999; Euler and Masland, 2000; Matsui et al., 1998; Saszik and DeVries, 2012). Thus, the output of the bipolar cell onto the ganglion cell includes both the intrinsic response properties of the bipolar cell and the actions of amacrine cells upon the bipolar cell. The bipolar cell is as much an integrating center as it is a conduit from outer retina to inner. The second controller of the ganglion cell response is direct input from amacrine cells. Amacrine cells occupy a central but inaccessible place in the retinal circuitry. Most are axonless neurons and their lack of a clear polarity makes it hard to recognize the sites of their inputs and outputs. Because of their multiple connectivity, they are hard to conceptualize: they feed back to the bipolar cells that drive them, they synapse upon retinal ganglion cells, and they synapse on each other (Figure 5; Dowling and Boycott, 1966; Eggers and Lukasiewicz, 2011; Jusuf et al.

(2012). We starved 8- to 11-day-old flies raised at 18°C and presented them with one odor at the permissive 23°C for 2 min in filter paper-lined tubes. They were then transferred PI3K targets into a new prewarmed filter paper-lined tube and immediately presented with a second odor at restrictive 33°C for 2 min. Flies were then returned to 23°C and tested for immediate memory. Aversive memory was assayed as described in Tully and Quinn (1985) with some modifications. Groups of ∼100 flies were housed for 18–20 hr before training in a 25 ml vial containing standard cornmeal/agar

food and a 20 × 60 mm piece of filter paper. Reinforcement was 120 V. Relative aversive choice experiments (Figure 5) were performed as described in Yin et al. (2009) with some modifications. Flies were prepared mTOR inhibition as above for aversive memory and were conditioned as follows: 1 min odor X without reinforcement, 45 s fresh air, 1 min odor Y with 12 60 V shocks at 5 s interstimulus interval (ISI), 45 s fresh air, and 1 min odor Z with 12 30 V shocks at 5 s ISI. Memory performance was tested by allowing the flies 2 min to choose between the odors presented during training. Performance index (PI) was calculated as the number of flies approaching

(appetitive memory) or avoiding (aversive memory) the conditioned odor minus the number of flies going the other direction, divided by the total number of flies Sclareol in the experiment. A single PI value is the average score from flies of the identical genotype tested with the reciprocal reinforced/nonreinforced odor combination. Odor acuity was performed as described in Burke et al. (2012). Fed flies were transferred to 33°C 30 min before a 2 min test of odor avoidance. Odors used in conditioning and for acuity controls were 3-octanol (6 μl in 8 ml mineral oil) with 4-methylcyclohexanol (7 μl in 8 ml mineral oil) or isoamyl acetate (16 μl in 8 ml mineral oil) with ethyl butyrate (5 μl in 8 ml mineral oil). Statistical analyses were performed using PRISM (GraphPad Software). Overall ANOVA was followed by planned pairwise comparisons between

the relevant groups with a Tukey honestly significant difference HSD post hoc test. Unless stated otherwise, all experiments are n ≥ 8. To visualize native GFP or mRFP, we collected adult flies 4–6 days after eclosion and brains were dissected in ice-cold 4% paraformaldehyde solution in PBS (1.86 mM NaH2PO4, 8.41 mM Na2HPO4, and 175 mM NaCl) and fixed for an additional 60 min at room temperature. Samples were then washed 3 × 10 min with PBS containing 0.1% Triton X-100 (PBT) and 2 × 10 min in PBS before mounting in Vectashield (Vector Labs). Imaging was performed on Leica TCS SP5 X. The resolution of the image stack was 1,024 × 1,024 with 0.5 μm step size and a frame average of 4. Images were processed in AMIRA 5.3 (Mercury Systems).

In cerebral cortex there is a ubiquitous regulation click here of NR2 subunit composition during development in which NR2B is the major NR2 subunit during the first postnatal week with NR2A expression increasing thereafter (Monyer et al., 1994, Sans et al., 2000 and Sheng et al., 1994). NR2B-containing NMDARs exhibit slower kinetics than NR2A-containing receptors (Williams et al., 1993) and are also selectively

blocked by ifenprodil and related compounds (Williams, 1993). Consistent with the expression changes in NR2 subunits, NMDAR currents at cortical synapses exhibit faster decay kinetics and reduced sensitivity to ifenprodil during development (Carmignoto and Vicini, 1992, Hestrin, 1992, Flint et al., 1997, Tovar and Westbrook, 1999, Kirson and Yaari, 1996 and Williams et al., 1993), demonstrating that synaptic NMDARs switch from those predominantly containing NR2B to those containing

NR2A. The switch in NR2 subunit composition is dependent on see more neuronal activity and experience. In primary visual cortex the developmental switch requires visual experience (Carmignoto and Vicini, 1992) and in dark-reared animals can be rapidly induced with only 1 hr of exposure to visual experience (Quinlan et al., 1999 and Philpot et al., 2001). Moreover, at synapses on hippocampal CA1 pyramidal neurons, synaptic activity can drive NR2A subunits into synapses (Barria and Malinow, 2002), and LTP induction in the neonate acutely drives the switch of synaptic NMDARs from NR2B to NR2A these containing (Bellone and Nicoll, 2007). The NR2B to NR2A switch causes important changes to NMDAR function, altering the amount of calcium influx through the pore and the types of proteins interacting with the intracellular domain of the receptor. These features regulate the type of long-term synaptic plasticity (LTP or LTD) that NMDAR activation can induce, although the exact relationship between NR2 subunits and the induction of LTP and LTD remains controversial (Bartlett et al., 2007, Liu et al., 2004, Morishita et al., 2007 and Xu et al., 2009). Despite the ubiquitous nature and critical roles of the NR2B-NR2A switch in cortical

synapse function and plasticity during development, the mechanisms for induction of the subunit switch have not been characterized. We now show that the acute activity-dependent subunit switch induced by an LTP induction protocol in hippocampal CA1 pyramidal cells requires activation of both NMDARs and mGluR5. Furthermore, we find that a signaling cascade involving PLC activation, release of calcium from IP3R-dependent stores, and PKC activity is required. However, unlike LTP-induced changes in AMPAR function, the activity-dependent switch in NR2 subunit composition does not require CaMKII or PKA activity. Using mGluR5 knockout mice, we confirm the requirement for mGluR5 in acutely driving the switch in CA1 hippocampus.