8 ± 1.0 cells per mouse; KO: 17.4 ± 2.1 cells per mouse; Figure 2A) and analyzed units (CT: n = 48; KO: n = 92) with significant activity on the track (place field peak > 1 Hz). Fine quantification Epigenetic signaling inhibitor revealed no differences in these responses across multiple measures (Figure 2; see also Figure S1). Specifically, single units in KO exhibited normal place field sizes (F(1,138) = 0.01, NS; Figure 2B), normal firing rates within place fields (F(1,138) = 0.56, NS; Figure 2C), no difference in the normal tendency of units to fire more in one direction than another (F(1,138) = 0.19, NS; Figure 2D),

and no difference in sparsity (F(1,138) = 0.85, NS; Figure 2E), which is a measure of the localization of place fields (Jung et al., 1994). In addition, no difference was observed in spatial information index (F(1,138) = 0.02, NS; Figure 2F), which measures how informative about position a spike from a place cell is (Markus et al., 1994), and spatial coherence (F(1,138) = 0.92, NS; Figure 2G), which measures the local smoothness of a firing rate pattern of spikes (Muller and Kubie, 1989). Next, to determine whether excitability might be evident in the precise timing of single spikes, we further examined run-time unit activity

on a finer timescale. Since hippocampal single units exhibit complex check details spikes, made up of a burst of several spikes occurring 2–10 ms apart (Quirk and Wilson, 1999), we first measured the number of spikes during bursts. Both KO and CT units exhibited similar numbers of spikes per burst (F(1,142) = 0.01, NS; Figure 2H) and a similar percentage of burst spikes (F(1,142) = 0.40, NS; Figure 2I). Interestingly, however, we found that

bursts in KO tended to be faster, as measured by burst interspike interval (CT: 5.70 ± 0.70 ms; KO: 4.99 ± 0.78 ms; F(1,142) = 29.16, p < 10−6; Figure 2J), and extracellular spike amplitude attenuation, which is associated with complex spikes (Harris et al., 2001 and Quirk and Wilson, 1999), was also increased in KO (CT: 2.84% ± 0.39%; KO: 5.93% ± 0.38%; F(1,142) = 31.36, p < 10−6; Figure 2J). Taken together, these results indicated that the spatial representation at the level of single aminophylline cells in KO appears to be preserved during exploratory behavior, in spite of the bias toward enhanced synaptic strength, with little change in spike timing during bursts. Since the place responses of single units in calcineurin KO were largely normal during run, we next examined whether unit activity during immobile periods, specifically SWRs, was also unaltered. In both KO and CT mice, single units exhibited spikes during SWR events (Figure 3A). Place cells in KO, however, fired more than double the number of spikes during each SWR event as compared to those in CT (CT: 1.11 ± 0.14 spikes per SWR; KO: 2.56 ± 0.54 spikes per SWR; F(1,81) = 4.84, p < 0.05; Figure 3B).

, 2006). To ensure that we compared the same labels by genes, we compiled a table where each record contains the following fields: Official Gene Symbol by Nomenclature The Entrez Gene Perifosine mouse ID was used as a key

field for comparison. The gene information was extracted from weekly updated gene information files on NCBI repository (ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/). The filter process is conducted in two main steps. First, comparison is done between our query data set and the filter data. We split the query list in two: potential filtered transcripts and potential dendritic/axonal transcripts. The second step is the assessment of false candidate after filtering. False-negative candidates arise in the filtered list due coexpression of those candidates in different cell types. Such records are identified and rescued by comparing the filtered list to transcripts that are present in hippocampus pyramidal PD-1/PD-L1 inhibitor 2 neurons (Sugino et al., 2006) or are identified by in situ methods either conducted by us (71 in situ probes) or by previous studies (Table S14). False-positive candidates arise in the cleaned list due to genes that were detected by 454 but were not present on the microarray chip from the reference studies. Those genes were checked in the Allen Brain Atlas for pyramidal neuron expression in area CAI of

the hippocampus. The genes that were de-enriched in the investigated area were pulled out of the result list and a false-positive rate was determined. The Gene Ontology analysis was conducted using the Bingo Plug-In (v 2.44) for Cytoscape (Maere et al., 2005). The Cytoscape output is a text file with the

following parameters: term id, term name, p value, x (number of genes from the query list annotated to a certain term), X (number of genes from the query list that are annotated to a specific ontology), n (total not number of genes annotated to a certain term by the rat genome database), N (total number of genes annotated to an ontology by the rat genome database). One file was generated per ontology (biological process, molecular function, and cellular component). We calculated cluster frequency, total frequency, fold change, for each term graph level, where: ClusterFrequency=100∗xX TotalFrequency=100∗nN FoldChange=Cluster FrequencyTotal Frequency. Three biographs with the ancestors of all overrepresented terms in the corresponding ontology were built. An application was developed in order to search for the shortest path from each overrepresented term to the root of its graph and assign the distance as the depth level for the term (Dijkstra, 1959). We used an additional custom application to combine the results from the three ontologies in one file. The file was imported to Microsoft Excel in order to obtain one table per query list. The table was sorted by depth level and fold change. All terms in the table are overrepresented with p value less than 0.

, 2003, Korogod et al., 2007 and Lee et al., 2007) supported a role for PKC in PTP, but the observation that inactive analogs have similar effects on PTP (Lee et al., 2008) and that some PKC inhibitors

do not affect PTP at all (Eliot et al., 1994, Lee et al., 2008, Reymann et al., 1988a and Reymann et al., 1988b) have blurred the role of PKC in PTP. Using a molecular genetic approach allowed us to overcome the limitations associated with the lack of selectivity of GSK1120212 price PKC inhibitors and activators and establish that PKC plays a crucial role in PTP. Our results establish that calcium-dependent PKC isoforms mediate most of the PTP at the calyx of Held, with PKCβ playing a more prominent role than selleck screening library PKCα (Figure 9A, top). This challenges the previously-held view that a calcium-independent PKC isoform mediates PTP (Saitoh et al., 2001). Previous studies at the calyx of Held found that phorbol esters induce translocation of PKCɛ and suggested that this calcium-independent isoform mediates PKC-dependent plasticity

at this synapse (Saitoh et al., 2001). Moreover, different PKC inhibitors were found to have very different effects on PTP. A broad-spectrum inhibitor (bisindolymalemide, BIS) and one that preferentially targets calcium-independent isoforms (Ro 31-8220) reduced PTP (expressed as fraction of PTP in control conditions) to ∼40% and ∼20%, respectively (Korogod et al., 2007). One interpretation of these results crotamiton is that PTP involves calcium-independent PKCs, which might be activated by a tetanus-dependent elevation of DAG rather than by calcium. This interpretation is, however, complicated, because PKC inhibitors do not readily penetrate brain slices, and slices must be soaked in high concentrations of the inhibitors for long periods of time prior to the experiment. In some cases, broad-spectrum inhibitors (chelerythrine) do not reduce the magnitude of PTP (Lee et al., 2008). Limitations associated with the use of PKC inhibitors in slice preparation raise the possibility that the differential efficacy of PKC inhibitors may reflect their ability to penetrate the slice, rather

than their isoform selectivity (Brose and Rosenmund, 2002). This seems to be a plausible explanation for the differential effects of PKC inhibitors, in light of our observation that the calcium-dependent isoforms PKCα and PKCβ account for most of the PTP. Our experiments provide insight into the mechanisms underlying PTP. Calcium measurements suggest that although calcium channels are briefly facilitated, this facilitation makes a short-lived contribution to PTP (Figure 5C). Facilitated calcium entry is still present in double knockout animals, indicating that it is not mediated by calcium-dependent PKCs. PTP at cultured superior cervical ganglion neurons is also mediated primarily by mechanisms that are independent of calcium channel facilitation (Mochida et al., 2008).

A wide range of plant is known to trick insects into pollination without providing a reward. To accomplish this feat, these plants all rely on being able to trigger and to exploit neural circuits underlying obligate and innate

attraction in the targeted insects. In short, the plants copy signals that the intended victims of the deception cannot afford to ignore. Although visual and tactile cues are in many instances important, most often the key to success resides with the plants being able to mimic odors of importance to the insects (Urru et al., 2011). Accordingly, deceptive plants can provide unique insights into what constitutes a critical resource to the targeted insect and what sensory cues mediate the attraction to this resource. The dead horse arum (Helicodiceros Obeticholic Acid supplier muscivorus) and the Solomon’s lily (Arum palaestinum) serves as excellent examples of how deceptive plants can be used to identify important odor ligands. The former produces a ghastly smell, reminiscent of rotting flesh and also attracts carrion blowflies (Diptera: Calliphoridae), the latter has in contrast a pleasant smell, similar to fruity wine and instead attracts drosophilid flies. The apparent carrion mimicry is remarkably simply accomplished, via the production of just three compounds, namely dimethyl mono-, di-, and trisulfide ( Stensmyr et al., 2002). The mimicry of alcoholic fermentation is likewise

accomplished via only a handful of odorants, including MG-132 concentration e.g., acetoin acetate and 2,3-butanediol acetate ( Stökl et al., 2010). The deception nevertheless works since the copied odors are diagnostic for the targeted insects favored oviposition sites (i.e., decomposing animals and rotting fruit respectively), whereas they are very rarely present in other substrates. These plants hence nicely demonstrate the principle that insects rely on a select set of chemicals to localize essential resources. Systems built on sensory deceit are thus excellent sources of information regarding key stimuli for

the dupe. The mimicking flowers produce odors to which olfactory receptors in insects very likely have evolved high affinity. Having access oxyclozanide to such ligands is of course of utmost importance when dissecting the neural function of the olfactory system, from periphery to brain, and further deepens our understanding of insect behavior. Investigations of such systems should be carefully selected among plants duping interesting target species. Vinegar flies is a natural candidate, but, relating to our suggestions above, finding flowers that target primitive insects as pollinators would be highly valuable, as would identifying plants/flowers that could be used as deceptive traps for insects of public health (e.g., mosquitoes) and agricultural economic concern (e.g., beetles). The insect olfactory system and its ability to evolve over relatively short time spans is probably an important part of the explanation why insects are such successful organisms.

AAQ-mediated retinal light responses are rapid. MEA recordings show that the median response latency of RGC spiking is 45 ms in the AAQ-treated rd1 mouse retina, compared to ∼50 ms (Farrow and Masland, 2011) to several hundred ms (Carcieri et al., 2003) for photopic light responses from RGCs in wild-type retina. Retinal chips electrically stimulate RGCs directly, and therefore can elicit spikes with latencies of several milliseconds. For optogenetic tools, depending on which retinal cell type expresses the tool, the response latency

of RGCs ranges from several milliseconds to 150 ms (Bi et al., 2006, Busskamp et al., 2010 and Lagali et al., 2008). Stem cell-based therapies would presumably Autophagy inhibitor supplier restore wild-type kinetics, assuming the differentiated rods and cones have full function. MEA recordings in vitro and PLR measurements in vivo indicate that the AAQ-treated rd1 mouse retina responds under bright photopic conditions, comparable to levels achieved in natural outdoor illumination. This is similar to light sensitivity conferred onto RGCs by optogenetic tools (Bi et al., 2006 and Thyagarajan et al., 2010). Exogenous expression of NpHR in cone remnants can result in higher light sensitivity (Busskamp et al., 2010). However, it is unclear whether many patients with advanced RP have sufficient cone remnants to allow this to be a broadly applicable approach (Milam et al., 1998). High sensitivity can also be conferred by exogenously expressing

CT99021 molecular weight melanopsin in RGCs that are not normally light-sensitive (Lin et al., 2008), but the responses are variable and slow (on the order of seconds). Stem cell-based therapies in theory might recapitulate the wild-type sensitivity of rods and cones. However, the human retina normally contains >100,000,000 rods and cones, and whether a significant fraction can be restored with stem cells remains unclear. AAQ-mediated retinal

responses have a high spatial resolution. Our spot illumination experiments places a 100 μm radius upper limit on the AAQ-mediated Parvulin receptive field size. Amacrine cells, which predominate in driving RGC responses, can project over several hundred μm, but mutual inhibition between these cells presumably spatially constrains RGC responses to a smaller area. Because AAQ is a diffusible small molecule, in principle it should reach the entire retina and confer light sensitivity on all RGCs. In practice, we observed robust light responses in almost all RGCs when AAQ was applied in vitro, but intravitreal injections in vivo were less effective, with only 25%–36% of injections resulting in behavioral responses to light. Drug delivery via intravitreal injections in mice can be unreliable because of the very small vitreal volume (20 μl), which is 250-fold less than the vitreal volume of the human eye (5.5 ml). Further experiments using animals with larger vitreal volumes are needed to better test and optimize the effectiveness of intravitreal AAQ administration.

A computerized tracking system was used to monitor CX-5461 price the position of the mice in the maze (Any-maze). The mice were positioned in the center of the plus facing an open arm and were

given 5 min to explore the maze. The amount of time spent in the closed vs. open arms was recorded. Functional performance of the mice on the behavioral tests was assessed using two-way analyses of variance (ANOVA) with Genotype and Treatment as between-group factors. Planned individual comparisons between different genotype groups (E3 vs. E4) and treatment groups (SedCon vs. SedEC vs. ExCon vs. ExEC) were performed using a single degree-of-freedom F tests involving the error term from the overall ANOVA. Performances were also considered in three-way with Session as the repeated measure. The effects of strain within the SedCon groups were analyzed using a one-way ANOVA with Strain (wild-type vs. E3 vs. E4) as a factor. Planned individual comparisons were performed using a single NLG919 manufacturer degree-of-freedom F tests involving the error term from the overall ANOVA. Pooling male and female data was not responsible for driving any of the main results. The α level was set at 0.05 for all analyses. The software used for the analyses was Systat 13 (Systat Software Inc., San Jose, CA,

USA). The performance of the mice as measured by path length and swimming speed is presented in Fig. 1. Path length Farnesyltransferase of all wild-type, E3, and E4 mice decreased as a function of sessions ( Fig. 1A). The effect of testing session on path length was confirmed by an analysis of variance with Session as repeated measure (p < 0.05). There was no effect of Strain or Treatment on the performance of the mice as supported by a lack of significant

main effects or interaction of Strain and Treatment (all p > 0.259). The wild-type C57BL/6 SedCon group took shorter path length than the E3 or E4 mice, especially between sessions 3 and 7. This was supported by an ANOVA revealing a main effect of Strain (p < 0.05). Overall, the E4 mice swam faster than the E3 ones, which was supported by a main effect of Strain (p < 0.05). The SedEC and ExEC mice seemed to swim slower than the controls throughout the sessions, however this was not supported by the analysis of the path-independent swimming speed yielding no main effect of Treatment (p = 0.057) or interaction of Strain and Treatment (p = 0.359). The wild-type and E4 mice swam faster than the E3 mice, which was supported by a main effect of Strain (p < 0.01) following a one-way ANOVA. Accuracy for spatial memory was measured by conducting a probe trial as the last trial of sessions 2, 4, 5, 7, and 9 (Fig. 2). All the mice tested developed a strong bias for the platform location (p < 0.05), however there was no difference between the performance of the E3 and E4 strains (p = 0.052) and no effect of Treatment (p = 0.067).

, 2007 and Newsome et al., 2000). We focused on the well-characterized loss-of-function fra3 allele ( Kolodziej et al., 1996), as eye development was not affected ( Figure S2). Within the optic lobe of these adult mosaic animals, 88.7% of fra3 mutant R8 axons, identified with the marker Rh6-lacZ (656 axons, n = 18), exhibited strong projection defects: 56.2% stalled at the medulla neuropil border, while 32.5% terminated prematurely at the more distal layers M1 and M2 ( Figures 2A–2C). In contrast, ganglion-specific targeting of R1–R6 axons to the lamina and layer-specific targeting of R7 axons to the M6 layer appeared

unaffected ( Figure S3). Single Rh6-lacZ-positive fra3 homozygous mutant R8 axons generated by mosaic analysis with a repressible BIBF 1120 in vitro cell marker (MARCM) ( Lee and Luo, 1999) showed fully penetrant phenotypes: they either stalled at the distal medulla neuropil border (5 of 17 clones) or terminated in the M1/M2 layers (12 Selleckchem EX527 of 17 clones), while neighboring heterozygous R8 axons terminated correctly in the M3 layer ( Figures

2D–2E″′). Hence, fra is required cell autonomously in R8 neurons for targeting to the correct layer. To assess at which targeting step these defects occur during metamorphosis, R8 axons were labeled with the early marker ato-τ-myc ( Bazigou et al., 2007 and Senti et al., 2003) ( Figures 2F–2N). From 24 hr APF onward, all wild-type R8 axons are located in the temporary layer at the distal medulla neuropil border, where they pause for approximately 30 hr before projecting to the emerging recipient layer M3 at around 55 hr APF ( Ting et al., 2005). In ey3.5-FLP mosaics, Calpain a small proportion of fra mutant R8 axons at 24 hr (12%, 526 axons, n = 16) and 42 hr (6.2%, 324 axons, n = 8) proceeded prematurely into the neuropil located between R8 and R7 growth cones. At 55 hr, the majority of mutant R8 axons stalled at the medulla neuropil border (91%, 255 axons, n = 10). This indicates that fra is mainly required during the second

targeting step to the final layer. Histological analysis and immunolabeling with available markers showed that the observed phenotypes are not the consequence of general eye development errors, R8 cell fate-specification defects, abnormal proliferation and differentiation of target neurons and glia, or earlier R cell-projection defects during the third-instar larval stage ( Figure S2). To test whether fra is also sufficient, we expressed this receptor in all R cells using lGMR-Gal4 as driver. This prolonged ectopic expression did not redirect R7 axons to the M3 layer, while many R8 axons (31.9% of 210 Rh6-lacZ-positive axons, n = 9) remained in the temporary layer ( Figures 2O–2Q). Thus, expression of Fra in R cells is not sufficient to alter target layer specificity. Focusing next on the activating ligands of Fra, Netrin-A and Netrin-B (NetA, NetB) (Harris et al., 1996 and Mitchell et al.

, 2008) or when postsynaptic spiking is prevented Depsipeptide during tetanic stimulation (Alle et al., 2001). Conversely, activity-dependent internalization of presynaptic mGluR7 receptors has been suggested to underlie a metaplastic switch from LTD to LTP (Pelkey et al., 2005). Pre- and postsynaptic intracellular signaling cascades at many glutamatergic synapses innervating interneurons are thus finely balanced and can be tipped toward one form of plasticity or the other depending on the state of the neuron and, presumably, the precise

conjunction of pre- and postsynaptic activity. Although much of what we know of plasticity of inhibition has emerged from studies in the hippocampus, related

forms of plasticity have been reported in several other regions of the mammalian brain. LTP in interneurons dependent on Ca2+-permeable AMPA receptors was first described in the amygdala (Mahanty and Sah, 1998), where it is restricted to interneurons that express NMDA receptors lacking NR2B subunits, although Ca2+ influx via these receptors appears not to contribute to plasticity (Polepalli et al., 2010). In contrast to NMDA receptor-independent plasticity in the hippocampus, the locus of expression of LTP in these cells appears to be postsynaptic. In the striatum, several interneurons have been shown to express STDP at synapses made by cortical glutamatergic afferents (summarized in Fino and Venance, 2011). In FS interneurons, for example, NMDA receptor-dependent LTP was elicited when the

presynaptic action potential selleck chemical preceded the postsynaptic spike and LTD when the order was reversed (Fino et al., 2008). This STDP rule is thus broadly similar to that seen in neocortical pyramidal cells. In FS interneurons of the somatosensory cortex, in contrast, one study reported mGluR-dependent LTD whether the presynaptic spike preceded or followed the postsynaptic spike (Lu et al., 2007). A similar pattern was observed at intracortical glutamatergic synapses these on regular-spiking interneurons in barrel cortex (Sun and Zhang, 2011). mGluR5 receptors also play a central role in NMDA-independent LTP of excitatory postsynaptic potentials in FS interneurons of the visual cortex (Sarihi et al., 2008). In contrast, low-threshold spiking cells in the same cortical area exhibit both NMDA receptor-dependent LTP with a “pre before post” protocol and mGluR-dependent LTD when the spike order is reversed. A further form of LTP induced by theta-burst stimulation has been reported in somatostatin-positive neocortical interneurons, which is insensitive to manipulation of postsynaptic Ca2+ channels or NMDA receptors and may therefore not involve postsynaptic signaling at all (Chen et al., 2009).

Thus, in line with our in vitro results, both in flies and in mammalian cells, loss of Lrrk/LRRK2 function results in increased association of EndoA with membrane, whereas gain-of-LRRK2 kinase activity impedes EndoA membrane association. Our results thus far allow us to make a number of predictions. First, given that inhibition of EndoA S75 phosphorylation facilitates membrane association, we expect flies expressing a phosphodead EndoA to harbor FRAX597 too much membrane-bound EndoA that impedes the endocytic process, similar to our observations in Lrrk mutants. Second, phosphorylation of EndoA S75 inhibits

membrane association of the protein and flies expressing a phosphomimetic EndoA are therefore predicted to also show reduced endocytosis. Third, because EndoA S75 phosphorylation in animals that express the kinase-active LRRK2G2019S

is increased, we also expect this condition to show reduced endocytosis. Fourth, we surmise that a specific LRRK2 kinase inhibitor will result in endocytic defects similar to Lrrk mutants and that this inhibitor does not exacerbate the endocytic defects in phosphodead EndoA but that it rescues the endocytic defects in LRRK2G2019S-expressing animals. To start testing these predictions, we expressed EndoA[S75A] and EndoA[S75D] using genomic fragments in endoAΔ4 null mutants ( Figure S6A) and determined endocytic efficiency. First, we stimulated larval fillets for 1 min in 90 mM KCl with FM1-43. Compared to endoA+/+; endoAΔ4 control third-instar larvae, both the endoAΔ4 animals that express the EndoA[S75A] phosphodead mutant, as well as the endoAΔ4 animals that express the EndoA[S75D] phosphomimetic Duvelisib solubility dmso mutant, show reduced synaptic vesicle endocytosis ( Figures 7A–7D). Our data indicate that both phosphorylation and dephosphorylation of EndoA at S75 inhibit FM1-43 dye uptake at synapses in vivo. To further test our predictions, we also used an independent pharmacological approach to inactivate LRRK2 activity. We incubated dissected control third-instar larval

fillets for 30 min with different concentrations of LRRK2-IN-1, an LRRK2 inhibitor (Deng et al., 2011), and determined synaptic endocytosis using FM1-43. Application of LRRK2-IN-1 results in tuclazepam a dose-dependent reduction in FM1-43 dye uptake (Figure S6B). Furthermore, defects in FM1-43 dye uptake are very similar in Lrrk mutants or in Lrrk mutants incubated with the inhibitor ( Figure S6C), indicating specificity of LRRK2-IN-1 to LRRK-dependent synaptic membrane uptake defects. In addition, LRRK2-IN-1-mediated inhibition of LRRK in animals that only express the phosphodead EndoA does not significantly exacerbate their FM1-43 dye uptake defect ( Figure S6D). Hence, reduced LRRK-dependent EndoA S75 phosphorylation results in reduced synaptic vesicle formation during stimulation. A high concentration of inhibitor is needed in these assays probably because of limited penetration into the Drosophila larval NMJ ( Miśkiewicz et al.

On the last day of physiology, BDA injections were made along the recording paths to estimate recording sites. We measured average firing rates, Z scores, precision,

and selectivity from the responses of individual neurons. Z scores were measured as (driven firing rate − baseline firing rate)/(SD of baseline firing rate). We quantified trial-to-trial precision by first computing the shuffled autocorrelogram using the spiking responses to individual songs ( Joris et al., 2006). The shuffled autocorrelogram quantifies the propensity of neurons to fire spikes across multiple presentations of the same stimulus at varying lags. The correlation index is the shuffled autocorrelogram value at a lag of 0 ms, and it indicates selleck chemical the propensity to fire spikes at the same time (±0.5 ms) each time the stimulus is presented. To quantify selectivity, we first determined the number of songs that drove at least one significant spiking event. Significant spiking events were defined by two criteria: (1) the smoothed PSTH (binned at 1 ms and smoothed with a 20 ms Hanning window) had to exceed baseline activity (p < 0.05), and (2) during this duration, spiking activity had to occur on >50% of trials. Selectivity was then quantified as 1 − (n/15), where n was the number of songs (out of 15) that drove at least one significant spiking event. To quantify population sparseness, we computed the fraction of neurons that produced significant

spiking events during every 63 ms epoch, using a sliding window. We then quantified the fraction of neurons active during each window, with low values indicating first higher levels of sparseness. To create population PSTHs, we first computed Galunisertib purchase the PSTH of each individual

neuron within a population in response to a single song, smoothed with a 5 ms Hanning window. We then averaged the PSTHs of every neuron in a population, without normalizing. To quantify the degree to which neural responses to auditory scenes reflected the individual song within the scene, we computed an extraction index using the PSTHs to a scene at a particular SNR, as well as the PSTHs to the song and chorus components of that scene. From these PSTHs we computed two correlation coefficients: Rsong was the correlation between the song and scene PSTHs and Rchor was the correlation coefficient between the scene and the chorus PSTHs. The extraction index was defined as (Rsong − Rchor)/(Rsong + Rchor). Other methods for quantifying the extraction index from the PSTHs or from single spike trains produced qualitatively and quantitatively similar results. STRFs were calculated from the spiking responses to individual vocalization and the corresponding spectrograms using a generalized linear model, as previously described (Calabrese et al., 2011). We validated the predictive quality of each STRF by predicting the response to a song not used during estimation. We then calculated the correlation coefficient between the predicted and actual PSTHs.