To verify that cue responsiveness did not result

from conditioned oro-motor responses, we performed multiple Roxadustat mw control analyses. First, we computed the power spectrum of the firing of cue-responsive neurons. Somatosensory neurons driven by oro-motor behaviors were identified on the basis of a known spectral signature (Katz et al., 2001): a characteristic peak in the 6–9 Hz band (the frequency of licking) in their firing frequency (Figure 3A, insets). Only 25.6% (20 of 78) of cue-responsive neurons were rhythmically modulated by oro-motor behaviors (black rectangles in the “Som”-labeled strip plot in right portion of Figure 3A). These neurons responded to the tone with a significantly (p < 0.05) longer latency (90 ± 15 ms, n = 16) than those without the somatosensory selleck screening library spectral signature

(50 ± 5 ms, n = 56). Because this method does not allow for the identification of potential somatosensory neurons that would not show rhythmic responses, a second analysis was performed on high-speed video recordings of the oro-facial region. To determine whether cue responses in neurons without somatosensory rhythmic signature preceded, or followed, mouth movements, the latency of the earliest detectable movement was determined with visual and automated methods in random subsets of sessions (Figures S3 and S7). The average latency of the earliest minimal mouth movements was significantly longer than that of tone-responsive neurons that did not have the rhythmic signature (automated methods: 187 ± 27 ms, p < 0.01, n = 10; blind visual inspection: 248 ± 29 ms, p < 0.01, n = 5). A session-by-session comparison of neural response and mouth movement latencies triggered by the cue confirmed that responses to cues systematically precede oral movements (Figure S3). This result is further confirmed by the inspection of population PSTHs in response to the earliest mouth movements (Figure S3), which shows a premovement ramp in firing

rates. Thus, a relatively large percentage of recorded GC neurons (19.6%, 58 of 298) produce responses to auditory tones that are not secondary to conditioned oral movements. Figures 3B and 3C show population PSTHs and representative examples of cue responses in nonrhythmic neurons. To determine 4-Aminobutyrate aminotransferase whether cue responses depended on learning, we quantified the number of neurons activated by the tone in six naive rats. In the first session in which the tone was introduced, only 1 out of 36 neurons recorded produced a nonsomatosensory tone response (2.8% versus 19.5% after training; p < 0.05) with a long latency (99 ms), suggesting that a high incidence of short latency cue-evoked activity could depend on learning and relate to the anticipatory value of the tone (Kerfoot et al., 2007). If the responses described above are truly anticipatory, they should result from top-down influences.

, 2003, Johnson et al., 2003, Sherry et al., 2003 and Stella et al., 2008) and is required for synaptic glutamate release (Hnasko et al., 2010 and Stuber et al., 2010), we mated ET33-Cre mice with mice that carry floxed alleles of VGLUT2 (Hnasko et al., 2010) in order to generate mice lacking VGLUT2 specifically in ipsilateral-projecting RGCs. In mice, VGLUT2 protein is expressed at low levels at P0 and increases dramatically

over the first postnatal week (Sherry et al., 2003 and Stella et al., 2008). We found that Cre expression in ET33-Cre mice starts embryonically at least as early as embryonic day 18 (Figure S1C) and when we cultured RGCs from postnatal day 3 (P3) ET33-Cre mice expressing either wild-type or floxed VGLUT2 and immunostained them on P5, we found that VGLUT2 immunofluorescence intensity was nearly absent from the ET33-Cre::VGLUT2flox/flox RGCs (Figures S2A–S2G). To determine if retinogeniculate transmission PF 01367338 was reduced in ET33-Cre::VGLUT2flox/flox mice, we measured electrophysiological responses of dLGN neurons in response to optic tract stimulation. We prepared brain slices containing the optic tract and dLGN, which allowed

us to stimulate RGC axons and record postsynaptic responses in whole-cell voltage-clamped dLGN neurons (Chen and Regehr, 2000: Koch and Ullian, 2010). The optic tract contains axons from both eyes, so by removing one eye from young mice and allowing the severed RGC axons to LY2157299 price degenerate we were able to prepare slices that contained either contralateral or ipsilateral axons, but not both (Figures 2A and 2B). We also injected CTb into the intact eye to visualize its projections in the slice, thus allowing proper targeting of the recording and stimulating electrodes (Figure 2B). Recordings were performed on P5 and P10. Stimulation of contralateral RGC axons in P5 slices produced postsynaptic NMDAR-mediated responses in every dLGN neuron tested, regardless of genotype. Indeed, the size of the contralateral NMDAR-mediated these responses was

indistinguishable between Cre-expressing and Cre-negative slices (Figures 2C and 2E; VGLUT2flox/flox = 1006 ± 138.69 pA, n = 11 and ET33-Cre::VGLUT2flox/flox = 1102 ± 176.1 pA, n = 11; p > 0.05 by Student’s t test). By contrast, when ipsilateral RGC axons were stimulated, dLGN neurons in ET33-Cre::VGLUT2flox/flox slices often failed to respond (11 responses out of 24 cells) and response sizes were reduced by ∼55% (Figures 2D and 2F; VGLUT2flox/flox mice = 343.75 ± 59.21 pA, n = 19 and ET33-Cre::VGLUT2flox/flox mice = 157.49 ± 40.51 pA, n = 22; p = 0.014 by Mann-Whitney U test). AMPAR-mediated responses showed similar results (Figures S2H–S2M). Next we assessed retinogeniculate transmission in slices from P10 mice, an age when ongoing spontaneous activity continues to refine and maintain eye-specific retinogeniculate projections (Chapman, 2000 and Demas et al., 2006).

We propose a model in which HBCs are released from this inhibition upon downregulation of p63, which allows these stem cells to differentiate into fully mature olfactory neurons and other cell types according to a prescribed differentiation program. Our discovery of p63 as a key regulator of HBC dynamics provides important insight into the cellular mechanisms regulating this multipotent neural progenitor and implicates a larger p63-dependent transcriptional network that drives cell fate decisions between self-renewal and differentiation in the postnatal olfactory

stem cell. Through what cellular mechanisms does p63 promote self-renewal of the olfactory stem cell? Unlike other epithelial stem cells, which are proliferative, HBCs are largely quiescent under normal conditions. Because p63 is expressed in both quiescent and proliferating learn more HBCs, if p63 is indeed required for self-renewing proliferation, it must work in concert with other factors in the cell to govern the transition from quiescence to proliferation. In other epithelial systems, p63 appears to support stem cell self-renewal in vivo by antagonizing apoptosis or senescence (Senoo et al., 2007 and Su et al.,

2009b). Similarly, in the central nervous system, MI-773 nmr p63 antagonizes p53 activity to promote

the survival of embryonic cortical neural precursor cells and newly born cortical neurons (Dugani et al., 2009). Although it is formally possible that p63 inhibits apoptosis of newly born HBCs in order to promote stem cell survival following HBC cell division, we did not in fact however observe a statistically significant increase in caspase 3-positive cells in the p63 mutant background. Future studies should help illuminate the contributions of p63 function to the processes of olfactory stem cell proliferation and stem cell survival. In the first studies demonstrating p63′s function in development, germline deletion of the p63 gene resulted in severe defects in the development of the skin and other stratified epithelia ( Mills et al., 1999 and Yang et al., 1999). One group observed a depletion of the proliferative stem cells but persistence of a small number of differentiated cells, which was interpreted to reflect a requirement for p63 in stem cell self-renewal, but not for differentiation ( Yang et al., 1999). The defects in epidermal stratification and the paucity of differentiated cells were ascribed to a depletion of the proliferative stem cells in the developing epithelium owing to the lack of self-renewal ( Yang et al., 1999).

We further hypothesized that the direction and rate of change of neural activity at the time of the go cue (the “neural velocity”) also relates to that trial’s RT. We investigated this possibility using a similar analysis to that above, but now correlating the neural velocity at the time of the go cue (vgo) projected onto the mean neural trajectory with RT ( Figure 4A). In order to isolate the effects of neural velocity from position, we grouped trials together that had similar neural positions, which was done by further segregating our data by delay period into 100 ms bins (justified by results in Figure S1G). As shown in Figure 4B, for

both RO4929097 supplier monkeys the histograms have medians significantly less Fasudil cell line than zero (p < 0.01; Wilcoxon signed-rank test). This is consistent with the hypothesis that the greater the rate of change of neural activity in the direction of the mean neural trajectory at the time of the go cue, the shorter the RT. We again performed

control analyses to rule out alternative hypotheses, as described in Figure S2. Specifically, we found that the overall neural speed (i.e., magnitude of velocity) did not provide a stronger correlate with RT and that the observed correlations did not derive solely from the correlation of neural position and neural velocity to each other (Figures S2A and S2B). We combined both neural position and velocity along the mean neural trajectory at the time of the go cue to construct a multivariate predictor of trial-by-trial RT. Since the mean neural trajectory changes direction around the time of the go cue (see Figure 3B), we projected both position and velocity onto two vectors each, defined by the mean neural trajectory at times both before

and after the go cue. The vector representing the mean trajectory prior to the go cue, p¯go−Δt’, was based on an offset of Δt′ chosen to maximize the average correlation as before (see Figure S1B). The four resulting already covariates (each of neural position and velocity projected onto each of the pre- and post-“go” directions) were used as inputs to a multivariate linear regression for RT. This model was compared with other RT predictors in the literature: the rise-to-threshold hypothesis (the best performing of three different definitions of the rise-to-threshold process is shown); the optimal subspace hypothesis; and an independent linear decoding method (see Experimental Procedures). The percentage of total data variance explained is shown in the bar graph in Figure 5. This method explained more variance for each data set, had the most targets with significant correlations, and explained approximately 4-fold more variance than the next best model overall.

Perfusion by regular Ringer’s solution served as a negative control. Total RNA was extracted after 7 hr and qPCR performed (Figure 1B). We detected significant increases in both KCNQ2 and KCNQ3 mRNA in neurons stimulated by selleck chemical 50 K+ or ACh (Figure 1C). For KCNQ2, the relative

expression levels in neurons treated with high K+ or ACh were 2.05 ± 0.44 (n = 4; p < 0.05), and 1.80 ± 0.19 (n = 4; p < 0.05), respectively. For KCNQ3 transcripts, they were 1.76 ± 0.51 (n = 4; p < 0.05) and 1.56 ± 0.22 (n = 4; p < 0.05), respectively. M-current (IM) amplitudes in SCG neurons were then quantified to assay expression of functional M channels. As in the previous qPCR experiments, neurons were perfused by 50 K+, ACh, or regular Ringer’s solution for 15 min, and after 1, 48, 60, or 72 hr studied under perforated-patch voltage clamp. We did not observe a significant difference of IM amplitudes between neurons treated with regular Ringer’s and 50 K+ solutions 1 hr after stimulation, but we observed significant upregulation

of IM amplitudes in neurons treated with 50 K+ solution after 48, Depsipeptide supplier 60, or 72 hr, indicating that altered expression of M channels is involved. We thus decided to measure IM amplitudes 48–60 hr after stimulation in this paper, and examples of IM traces recorded from such neurons before and after application of the M-channel-specific blocker, XE991 ( Zaczek et al., 1998), are shown in Figure 1D. IM amplitudes were normalized to membrane capacitance and the current density used to indicate expression of functional M channels. In neurons treated with 50 K+ or ACh-containing solutions, IM amplitudes were significantly Florfenicol augmented ( Figure 1E). For neurons treated with regular Ringer’s or 50 K+-containing solutions, the current densities were 0.78 ± 0.10 pA/pF (n = 14) and 1.24 ± 0.12 pA/pF

(n = 14; p < 0.01), respectively. For neurons treated with regular Ringer’s or ACh, the current densities were 0.81 ± 0.06 pA/pF (n = 12) and 1.25 ± 0.15 pA/pF (n = 14; p < 0.01), respectively. NFAT signaling is critical to neural development and axon growth (Graef et al., 2003), as well as transcriptional regulation of several voltage-dependent K+ channels, e.g., upregulation of KV4.2 mRNA in cardiomyocytes (Gong et al., 2006) and downregulation of KV2.1 mRNA in arterial smooth muscle (Amberg et al., 2004). In the rat SCG neurons that we study here, NFATc1–NFATc4 has been shown to be expressed and, when activated, to translocate from cytoplasm to nucleus by electrical stimulation and kinase inhibitors (Hernández-Ochoa et al., 2007). We performed qPCR on SCG neurons and detected transcripts for NFATc1–NFATc4 isoforms (data not shown). We then asked which transcription factors mediate the upregulation of M-channel expression seen here, hypothesizing activity-dependent production of Ca2+/CaN and NFAT activation to be crucial.

It is important to note that DV is orthogonal to potentially confounding variables like expected value, uncertainty, choice confidence, or task difficulty. These variables are based on absolute values of DV (i.e., |DV|) and therefore, both highly negative (high evidence for counterclockwise gratings) and highly positive values of DV (high evidence for clockwise gratings) result in a high expected value (as well as high confidence, low difficulty, and low uncertainty), click here whereas values close to zero

result in a low expected value (as well as low confidence, high difficulty, and high uncertainty). Thus these variables encompass a U- (expected value, choice confidence) or inverted U-shaped (uncertainty, difficulty) relationship with stimulus orientation and hence DV. Functional MRI data were acquired on a 3-Tesla Selleckchem PD-332991 Siemens Trio (Erlangen, Germany) scanner. In each scanning run 341 volumes were acquired (TR = 2 s, 24 slices, 4.4 mm thick, in plane resolution 2 × 2 mm). Preprocessing was

performed by using SPM2 (Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, UK) and included slice-time correction, realignment, and spatial normalization to a standard template (resampling to 3 mm isotropic voxels). Spatial normalization was used to ensure that data from both scanning days are in a common reference space. We used a searchlight approach that allows whole-brain information mapping without potentially biasing voxel selection (Haynes et al., 2007, Kahnt et al., 2010 and Kriegeskorte et al., 2006) in combination with linear SVR. In a first step, for each subject, general linear models (GLM) were applied

to the preprocessed functional imaging data of each run. The GLM contained 11 regressors for different stimulus orientations (41°, 42.6°, Linifanib (ABT-869) 43.6°, 44.2°, 44.5°, 45°, 45.5°, 45.8°, 46.4°, 47.4°, and 49°) and four regressors accounting for left and right button presses and positive and negative feedback, respectively (all convolved with a canonical hemodynamic response function) as well as six regressors accounting for variance induced by head motion. The voxel-wise parameter estimates represent the response amplitudes to each of the 11 orientations in each of the 12 scanning runs. These parameter estimates were then used as input for the SVR and deviations from 45° were used as labels. SVR was performed by using the LIBSVM implementation (http://www.csie.ntu.edu.tw/∼cjlin/libsvm/) with a linear kernel and a preselected cost parameter of c = 0.01. For each searchlight (all voxels within a radius of 4 voxels surrounding the central voxel) we performed a 12-fold leave-one-out cross-validation. In each fold, training was based on data from 11 scanning runs and prediction accuracy was obtained in the independent 12th scanning run.

S. Department of State (A.M.B.). R.O. and J.-M.S. gratefully acknowledge the support of the BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs and the Netherlands Ministry of Education, Culture, and Science. “
“Distinct behaviors are associated with distinct patterns of activity, presumably underlying specific cognitive functions. In the hippocampus, immobility, slow-wave sleep, and automatic behaviors (sniffing, chewing,

champing, grooming, etc.) are accompanied by intermittent sharp-wave ripples (SPW) at high frequency (150–200 Hz), while locomotion, attention and REM sleep are accompanied by continuous theta oscillation (5–10 Hz) (Vanderwolf, 1969; Whishaw, 1972). These oscillations have been hypothesized to provide a common temporal reference for distributed neurons and support temporal coding, as illustrated in place cells’ theta phase precession where selleck screening library the phase of a cell’s spikes, relative to theta oscillations, systematically varies with the animal’s position VX-809 within its place field, so that additional spatial information is specifically

carried by the phase of spike discharge within the theta cycle (Huxter et al., 2003, 2008; O’Keefe and Recce, 1993; Skaggs et al., 1996). Therefore, depending on brain state, neuronal populations oscillate at various frequencies which regulate the timing of neuronal interactions to support neuronal encoding and information processing. A related phenomenon, although less well understood, lies in the cyclic modulation of LFP oscillations or cross-frequency

coupling (Bragin et al., 1995; Chrobak and Buzsáki, 1998). Recent studies in humans and monkeys emphasized the expression of rhythmic modulations of only neuronal oscillations (Canolty et al., 2006; Drew et al., 2008; Lakatos et al., 2005; Lakatos et al., 2008; Leopold et al., 2003; Nir et al., 2008), but the potential involvement of these “second-order” rhythms in information coding remains an open question. Here, we report slow modulation of theta power and neuronal firing at a time scale of about 1 s, expressed in the rat hippocampus during both REM sleep and motor/cognitive behaviors. We show that second-order spectral analysis is relevant for decoding neuronal information on a slower time scale than usually assumed for brain rhythms. Recording spontaneous LFP and unit activity from the hippocampal CA1 pyramidal layer of freely behaving rats during various behavioral situations including REM sleep, open-field exploration and a protocol in which the animals were trained to alternatively run in a maze and in a wheel to get a reward (data recorded in and provided by the Buzsáki lab [Pastalkova et al., 2008]), we observed that theta oscillations were not stable in amplitude but rather increased and decreased on a reproducible time basis of about 1 s.

The transplanted cells had Temsirolimus extensive and ramified processes (

Figures 1C–1F). We estimate that ∼2.7% of transplanted MGE cells survived 1 month after transplantation (1380 ± 478 cells per animal, n = 5). However, given the likelihood that many cells were lost in the course of injection (cells remaining in the injection pipette, cell death in the course of the injection, cells trapped in the pia, etc.), it is likely that the MGE survival rate is significantly underestimated. The majority of the GFP+ cells (∼71%) were located in the deep dorsal horn of the spinal cord (laminae III-V), over ∼2.5 mm of the rostro-caudal lumbar enlargement, all ipsilateral to the injection side. We did not detect any GFP+ cells in the thoracic or cervical spinal cord. Most GFP+ cells colabeled for NeuN, a marker of neurons (89.4% ± 2.7%, Figures 2A–2C), but none for Iba1, a marker of microglia ( Figures 2D–2F), or glial fibrillary acid protein (GFAP), a marker of astrocytes ( Figures 2G–2I), indicating that the vast majority of MGE-derived cells differentiated into neurons. By following the grafted cells from 1 to 5 weeks after transplantation, we conclude that it takes at least 2 weeks

for the MGE-derived cells to acquire a neuronal (NeuN+) phenotype ( Figures 2J–2L). The majority of the GFP+ MGE cells expressed markers of subpopulations of cortical GABAergic interneurons, including GABA (75.1% ± 9.6%, Figures 3A–3C), neuropeptide Y (NPY; 33.4% ± 9.1%, Figures 3D–3F), parvalbumin (PV; 22.2 ± 2.3%, Figures 3G–3I), and somatostatin (40.1% ± 4.1%, Figures 3J–3L). The presence of somatostain (SST)-GFP-positive check details neurons is of particular interest as this

neurochemical phenotype is characteristic of a large percentage (∼40%) of MGE-derived cortical GABAergic interneurons (Alvarez-Dolado et al., 2006). By contrast, GABA does not colocalize with SST in spinal cord interneurons; SST in fact marks a subpopulation of excitatory interneurons (Yasaka et al., 2010). These results provide evidence that the environment of the spinal cord does not alter the differentiation of MGE-derived cells into phenotypes similar to those observed in cortex. Taken together, these results indicate through that MGE transplants are viable in a foreign tissue environment (spinal cord versus cortex) and the majority of grafted cells differentiate into GABAergic neurons, which recapitulate the normal heterogeneity of cortical (but not spinal) GABAergic interneurons. As peripheral nerve injury induces a plethora of changes in the dorsal horn, including a profound activation of potentially phagocytic microglia, we next compared MGE graft survival in uninjured mice and others that underwent partial nerve injury (spared nerve injury, SNI). Survival rate of MGE cells in SNI animals (∼1.3%; 667 ± 267 cells per animal, n = 5) was in fact significantly lower (50% less) than in naive animals.

Abrupt resetting of the pattern occurred at the turning point

between each corridor, suggesting that salient landmarks or environmental features may reset the periodic pattern, resulting in a fragmented map. Based on the ability of the grid pattern to fragment in a complex environment, the encoding capacity of the grid network might also increase by representing environments as mosaics of smaller spatial maps (Derdikman and Moser, 2010 and Derdikman et al., 2009). In large environments, smaller maps may split along salient environmental borders or features. Readout of location from multiple map fragments may then rely on a mechanism or Angiogenesis inhibitor brain system substantially different from the metric readout of grid and place cells. Finally, a functional understanding of grid cells would need to incorporate the fact that natural environments have a three-dimensional topography that is very different from the flat, unconstrained surfaces that rodents explore in the laboratory. A recent paper probed how grid cells map to the vertical dimension by requiring rats to explore a helix-shaped track as well as a vertical surface lined with protruding horizontal pegs (Hayman et al., 2011). In these environments, grid fields

appeared as vertical columns, suggesting that grid cells do not differentiate between x,y positions with different z coordinates. However, it cannot presently be ruled out that periodic fields would reappear if animals are allowed to move continuously in the vertical plane, in the same way that they move Doxorubicin on horizontal surfaces. Another possibility is that the scale of grid cells is larger in the vertical plane and that the column structure simply reflects a stretched grid with a significantly larger field GBA3 size. Testing these possibilities is challenging but may be possible in species other than rats and mice. Our understanding of spatial representation in the hippocampal-entorhinal system has been strongly influenced by computational models. Models have proposed possible mechanisms for formation and transformation of

spatial firing patterns, and they have constrained the ways in which such patterns can be generated in circuits with known properties. Each model that we have described makes a number of testable predictions, but verification and falsification have so far remained indirect in that the experimental evidence is mostly correlational and subject to multiple interpretations. With the development of more sophisticated experimental methods during the next few years, the interaction between theory and experiment will likely be strengthened. Direct testing of models of grid cells may require quantitative analysis of the intrinsic dynamics and connectivity of individual neurons, and it may be necessary to activate as well as inactivate specific inputs to these neurons.

Brazil’s national immunization program provides vaccines included in the recommended immunization inhibitors schedule through the Unified Health System [Sistema Unico de Saúde (SUS)], Brazil’s public health system. State governments have autonomy to purchase and provide vaccines not included in the national immunization program through the state immunization program. Bahia, with a population of 13.6 million inhabitants, ranks fourth most populous among Brazil’s 27 see more states (including the Federal District) and had an annual

estimated health budget of US$ 1.5 billion in 2010 [6]. In February 2010, MenC-tetanus toxoid conjugate vaccine (MenC-TT, Neisvac-C®, Baxter Vaccines) was introduced into the routine infant immunization schedule in the state of Bahia, Brazil, with financing from the state government. After August, 2010, infants began receiving MenC-CRM197 Pexidartinib conjugate vaccine (Novartis Vaccines), which was provided to all states for universal infant immunization through Brazil’s national immunization program. The recommended schedule in all state immunization programs was two doses in the first year of life (either at 2 and 4 months or 3 and 5 months of age), followed by one dose in the second year of life (at 12 or 15 months). Catch-up vaccination was provided for children younger

than two years first of age in most states. In the state of Bahia, catch-up vaccination included children younger

than five years; one dose of MenC was recommended for those at least 12 months of age in February 2010. In addition, the state of Bahia purchased 1,876,863 doses of MenC-TT in 2010 to control the epidemic of meningococcal serogroup C disease in Salvador, the state capital and most populous city (estimated population 2,676,606, 21% of the state population). MenC-TT vaccine was used for mass vaccination of persons 10–19 years old in May and June 2010. In August 2010, the state government received 447,983 doses of MenC-CRM197 from Brazil’s national immunization program, which were used for mass vaccination of persons 20–24 years with a single dose. Children 5–9 years of age were not vaccinated. MenC vaccination was offered at 52 vaccination posts throughout the city. Vaccination was offered on Saturday and Sunday at the beginning of each phase to minimize disruption of normal vaccination services. Social mobilization focused on the first two days of vaccination for each age group. Due to poor turnout among 20–24 year olds in 2010, vaccination was offered for persons in this age group during the second weekend in February 2011, and at large universities the following week. MenC doses administered by age group at each vaccination post were reported to the immunization unit of the Salvador municipal health department.