Support for this class of models has come from the analysis of grid cells in the entorhinal cortex (de Almeida et al., 2012), a region that provides input to the hippocampus and has been previously implicated in working memory (Gaffan and Murray, 1992; Ranganath

and D’Esposito, 2001; Stern et al., 2001; Suzuki et al., 1997; Young et al., 1997). It was found that the entorhinal cortex has a working memory mode in which find protocol grid cells represent the recent past (i.e., positions behind the animal). Consistent with the model of Figure 1B, cells representing different positions fired in different gamma subcycles of the theta cycle. Another way of asking whether the theta-gamma code underlies working memory is to relate the oscillations to the psychophysically measured properties of working memory. A classic result (Miller, 1956) is that working memory has a capacity limit (span) of 7 ± 2 (see Cowan [2001] for a slightly lower value). The number of gamma cycles within a theta cycle may be what sets the capacity limitation for working memory (Lisman and Idiart, 1995). Initial efforts

to test this concept sought to use the theta-gamma framework to quantitatively account for response time properties of the Sternberg task (i.e., time to respond to whether a given test item was on a short list presented several seconds before). Selleck Inhibitor Library The linear dependence of response no time on the number of items in working memory suggested that the list was serially and exhaustively scanned at a rate of 20–30 ms per memory item (Sternberg, 1966), a time that approximately equals the duration of a gamma cycle. These and other quantitative results of the Sternberg task can be accounted for by models based on the theta-gamma code assuming either that theta phase is reset by stimuli or that theta frequency decreases with memory load (Jensen and Lisman, 1998). Experiments provide evidence for both effects (Axmacher et al.,

2010; Moran et al., 2010; Mormann et al., 2005; Rizzuto et al., 2006). Recent work sought to determine whether properties of theta and gamma oscillations in individuals could explain their memory span. The ratio of theta to gamma (i.e., the maximum number of gamma cycles within a theta cycle) was found to correlate with span (Kamiński et al., 2011). However, the determinations of oscillation frequencies were very noise sensitive, raising doubts about the conclusion. Rigorous testing of this relationship will require resolution of the controversy about which brain regions are responsible for short-term memory maintenance and better methods for noninvasive measurement of the oscillatory frequencies at those locations.

Another study showed that students self-imposed costly deadlines to avoid procrastination (Ariely and Wertenbroch, 2002). That people do this suggests they are sometimes aware of potential temptations, which makes (costly) precommitment decisions more valuable in the long run relative to unconstrained decisions, which are vulnerable to (more costly) self-control failures. Even though precommitment is widely used as a self-control strategy outside of the laboratory, and has been the subject of extensive theoretical consideration (Elster, 2000), compared to willpower Ku-0059436 order it has received far less attention from the empirical behavioral sciences

(Fujita, 2011), and the neural mechanisms of precommitment remain unknown. In the current study, we developed a behavioral method to directly test the effectiveness of precommitment relative to willpower. We used this measure in conjunction with fMRI to investigate the neural mechanisms of precommitment and its relationship to other varieties of self-control. Previous studies of the neural basis of self-control have focused primarily on willpower. These studies have consistently implicated the dorsolateral prefrontal cortex (DLPFC), inferior frontal gyrus (IFG), and posterior parietal cortex (PPC) in the effortful inhibition of impulses during self-controlled decision making (McClure et al., 2004,

McClure et al., 2007, Hare et al., 2009, Figner et al., 2010, Kober et al., 2010, Essex et al., 2012 and Luo et al., 2012). These findings converge with those of studies employing measures selleck products of the ability to inhibit prepotent motor responses, which also implicate the DLPFC and IFG (Aron et al., 2004, Chikazoe et al., 2007, Simmonds et al., 2008 and Cohen et al., 2012). In line with these studies, we expected to find increased activation in DLPFC, IFG, and PPC when subjects deployed willpower to actively resist temptations. Meanwhile, ADAMTS5 the neural basis of self-control by precommitment remains unexplored. Precommitment is nonnormative, in the sense that a rational decision maker with time-consistent preferences should

never restrict his choice set. But precommitment is adaptive when willpower failures are expected. Thus, an optimal precommitment strategy should require information about the likelihood of willpower failures. One computationally plausible neural mechanism is a hierarchical model of self-control in which an anatomically distinct network monitors the integrity of willpower processes and implements precommitment decisions by controlling activity in those same regions. The lateral frontopolar cortex (LFPC) is a strong candidate for serving this role. A recently proposed framework of executive decision making places frontopolar cortex at the top of a cognitive control hierarchy, enabling goal pursuit by orchestrating diverging action plans represented in caudal and lateral prefrontal regions (Burgess et al.

In one model of the cerebellar microcircuit, a sparse representation of time in the granule cell population

provides the excitatory drive for Purkinje cells. Different granule cells would provide inputs to Purkinje cells at different times during a movement so that visually-driven climbing fiber inputs could potentiate or depress the granule-Purkinje synapses that were active 100 ms prior to the arrival of the climbing fiber signal (Buonomano and Mauk, 1994). Thus, the cerebellum could act independently in learning motor timing, or inputs from the FEFSEM could contribute to the temporal sparseness of the granule cell population in a way that is enhanced by learning in the FEFSEM. Recent work also has highlighted the possibility that learning occurs on different time scales (Lee and Schweighofer, 2009, Ethier et al., 2008, Smith Doxorubicin mouse et al., 2006 and Yang and Lisberger, 2010) with the possibility of very rapid short-term

learning in the cerebellar cortex as a prelude to slower, longer-term changes in the FEFSEM. Neurophysiological studies of motor and perceptual learning reveal a common theme: changes are localized to neurons whose properties best capture the features of the training stimulus (Arce et al., 2010, Paz et al., 2003, Recanzone et al., 1993, Schoups et al., 2001 and Yang and Maunsell, 2004). In real life, the learning rule can be very complex. Thus, the dimensionality of the neural representation of movements limits the flexibility Selleck Screening Library of the motor system in terms of what can be learned quickly. For many years, it was commonly believed that the responses of motor

cortex neurons could be modeled by a time-invariant combination of limb kinematics and dynamics (Evarts, 1968, Georgopoulos et al., 1982 and Moran and Schwartz, 1999). Recently, examination of a broader population of neurons in primary motor cortex (M1), dorsal premotor cortex (PMd), and the FEFSEM has revealed considerable heterogeneity in movement-related neural responses (Hatsopoulos et al., 2007 and Churchland and Shenoy, 2007). Many neural response patterns are explained poorly by standard MTMR9 eye movement parameters such as acceleration, speed, and direction. We propose that the FEFSEM and other motor cortices are important for facilitating action selection. The FEFSEM encodes smooth pursuit movements flexibly along seemingly baroque but perhaps behaviorally relevant dimensions, such as time, so that error and reward signals can act selectively on a subregion within the movement space to drive rapid, precise motor learning. Two male rhesus monkeys (Macaca mulatta) aged 6 and 8 years, tracked smoothly moving targets in exchange for a water reward. Both monkeys had prior experience in experiments on pursuit, but neither had participated in learning studies.

A ventral focus was evident in the explore participants and also across the entire group but did not differ reliably between learn more groups. The more ventral

focus is closer in proximity to both the region of RLPFC associated with exploration by Daw et al. (2006) and the region associated with tracking reward value of the unchosen option by Boorman et al., (2009; though see Supplemental Information for an analysis of branching and the expected reward of the unchosen option in the current task). We did not obtain region by effect interactions and so are not proposing that a functional distinction exists between these dorsal and ventral subdivisions. Nevertheless, activation clusters in these two subregions were clearly spatially noncontiguous and were reliable under partially overlapping contrast conditions. Thus, future work should be careful regarding the precise locus of effects in RLPFC and their consistency across conditions. Beyond RLPFC, we also consistently located activation in SPL in association with relative uncertainty in the explore BVD523 group. Although this region was not reliably different between explorers and nonexplorers, the relative uncertainty effect was found to be reliable in SPL in explorers across the alternate models tested

here. Previous studies have reported activation parietal cortex along with RLPFC during tasks requiring exploration (e.g., Daw et al., 2006). However, the locus of these effects has been in the intraparietal sulcus

(IPS) rather than in SPL. Effects in IPS were less consistently observed in the current study, and ROI analysis of IPS defined from previous studies failed to locate reliable relative uncertainty effects in this region (see Supplemental Information). This comes in contrast to the effects in RLPFC, which are highly convergent in terms of neural locus. The reason for the variability in parietal cortex cannot be inferred from the present data set. However, one hypothesis is that it derives from differences in attentional demands between the different tasks. For example, SPL has been almost previously associated with endogenous, transient shifts of spatial and object-oriented attention (Yantis et al., 2002 and Yantis and Serences, 2003), perhaps as encouraged by the clock face design, and thus, the direct relationship between exploration and identification/attention to new target locations on the clock. However, such hypotheses would need to be tested directly in subsequent experiments. Previous studies have not found an effect of uncertainty on exploration (Daw et al., 2006 and Payzan-LeNestour and Bossaerts, 2011).

Indeed, EMA studies have shown no relationship between smoking and negative affect (Carter

et al., 2008, Shiffman et al., 2002 and Shiffman et al., 2004a). At the same time, EMA data (Shiffman et al., 1996 and Shiffman and Waters, 2004) show that this association is quite strong when smokers http://www.selleckchem.com/products/MDV3100.html are quitting. These context-specific differences underscore how such associations may vary according to abstinence status and phase of smoking; thus, the relationships described by the WISDM during ad lib. smoking may not apply when smokers are quitting. This issue requires further study. We had hypothesized that ITS would have more jagged or scattered profiles of motives, emphasizing a few particular motives over others, but this was not supported. It appears that individual ITS, like DS, smoke for multiple reasons, rather than for just one or two. This diversity of motives may strongly root smoking in ITS’ behavioral repertoires, helping to explain I-BET151 ic50 why they have so much trouble giving up smoking, as indicated by analyses of national data (Tindle and Shiffman, 2011) showing that almost 80% of ITS’ quit efforts fail. Surprisingly, CITS and NITS did not differ in their profile across the standardized WISDM motives. However, CITS scored higher on PDM, and lower on SDM, mirroring in a more subtle way the pattern seen for DS.

Despite their history of daily smoking, CITS differed from DS much in the way NITS did. This suggests that, regardless of past history of daily smoking, individuals who now smoke intermittently emphasize situational motives to smoke, more than motives reflecting constant smoking or loss of control. The cross-sectional

design of this study precludes knowing whether the observed differences in smoking motives are causes or effects of subjects’ smoking status. Thus, we cannot say whether CITS’ profile of motives shifted as they changed from DS to CITS, or whether their NITS-like Adenylyl cyclase profile reflects a pre-existing variation in motives that enabled them to evolve from DS to ITS. Similarly, it remains unclear whether smokers who start their careers with a particular motives profile are able to avoid progressing to daily and dependent smoking and thus become ITS, or, alternatively, whether all smokers begin with similar profiles, but the profile shifts as most progress to daily smoking. Fundamentally, then, this study cannot determine the underlying causal factors that make some smokers DS and others ITS. It is likely that genetic factors play some role (Sullivan and Kendler, 1999). However, given ITS’ rapidly increasing prevalence in the last decade alone, environmental forces such as smoking restrictions likely promote ITS’ smoking behavior, perhaps interacting with genetic factors (Boardman, 2009, Boardman et al., 2010 and Shiffman, 2009). The study does, however, shed light on the dependence and motives of ITS.

Using an AAV1-GFP construct, there appeared to be labeling of a variety of cell types within the cochlea, including the inner hair cells and supporting cells using an anti-GFP antibody (Figure 1A), in a pattern similarly described by other investigators (Jero et al., 2001 and Konishi et al., 2008). Subsequently, virus containing the VGLUT3 gene (AAV1-VGLUT3)

check details was microinjected into the cochlea using two different techniques: initially via an apical cochleostomy (CO) and subsequently by direct injection through the round window membrane (RWM) (Figures 1B–1E). After delivery, RT-PCR of inner ear tissue (Figure 1C) demonstrated strong VGLUT3 mRNA expression in the rescued whole cochlea, organ of Corti, stria vascularis, vestibular neuroepithelium, and very weakly in the spiral ganglion. Noninjected cochleas of knockouts do not demonstrate VGLUT3 expression as noted (Figure 1C, KO −/+RT). In contrast, under immunofluorescence, inner hair cells were

the only cells labeled with anti-VGLUT3 antibody (Figure 1B). To determine the dose dependence of VGLUT3 expression in the IHCs, we injected either 0.6 μl or 1 μl of AAV1-VGLUT3 (2.3 × 1013 virus genomes [vg]/ml) into the cochlea (Figures 1D and 1E). Microinjecting 1 μl of virus resulted BMS 354825 in 100% of IHCs labeled with anti-VGLUT3 antibody; in contrast, microinjecting 0.6 μl resulted in only ∼40% of IHCs labeled by the antibody. We next sought to determine whether earlier viral delivery would result in more robust VGLUT3 expression (Figures 1D, 1E, and 2). As shown, delivery of virus via the RWM at postnatal day 10 (P10) results in ∼40% of the IHCs expressing VGLUT3 (Figures 1D, 1E, and 2), whereas similar doses (0.6 μl) of virus injected at P1–P3 results in 100% of IHC transfected in all Oxalosuccinic acid animals (Figures 1D,

1E, and 2). After verifying successful transgene expression within the IHC without significant organ of Corti injury, we next sought to determine whether the reintroduction of VGLUT3 would lead to measureable hearing recovery (Figure 3). In these studies, only 0.6 μl of AAV1-VGLUT3 was delivered at P10–P12. Auditory brainstem response (ABR) thresholds were first measurable within 7 days after viral delivery, with near normalization of thresholds to wild-type (WT) levels within 2 weeks (P24–P26) (Figures 3A–3C). Initially a CO technique was used for viral delivery. However, this method restored hearing in only ∼17% of animals (n = 5 out of 30 animals attempted), presumably because it was more technically challenging and due to the trauma of the approach (see Discussion). As a result, the method was subsequently changed to an RWM delivery, which resulted in hearing restoration in 100% of mice (n = 19 out of 19 mice). The time course of hearing recovery was similar for the CO (when successful in 17%) and the RWM delivery techniques (100% of mice). Compound action potentials (CAPs) were also restored within 7–14 days of viral delivery (Figure 3A).

, 1997 and Seeburg et al., 1998). Functionally, the adenosine deaminase enzyme ADAR2 is responsible for RNA editing that recodes a glutamine to arginine in the selectivity filter of GluR-B subunits (“Q/R editing” of GluR-B); consequently, ADAR2 knockout mice exhibit increased Ca2+ permeability, with concomitant epilepsy and death (Higuchi et al., 2000). More broadly, a generalized dysregulation of brain RNA editing in humans may contribute to epilepsy, depression, and suicidal tendencies (Gurevich et al., 2002, Schmauss, 2003 and Sergeeva et al., 2007). Indeed, it is BMN 673 purchase likely that numerous other editing

substrates remain to be identified in the mammalian brain, given the high inosine content DAPT molecular weight of mRNA in neural tissue (Paul and Bass, 1998). In particular, we wondered whether RNA editing might fine-tune the calmodulin (CaM) regulation of voltage-gated calcium channels (VGCCs). This Ca2+ feedback regulation would be an especially attractive target for editing, because structure-function analysis

reveals that even single amino acid substitutions at critical channel hotspots can markedly alter modulatory properties (Dick et al., 2008, Tadross et al., 2008 and Zühlke et al., 2000), and such regulation impacts functions as diverse as neurotransmitter release, neuronal pacemaking, neurite outgrowth, and gene expression (Dunlap, 2007). Figure 1A cartoons such regulatory hotspots, which are located

on the amino- and carboxyl-termini of the pore-forming α1 subunits of VGCCs. The best-studied locus is a CaM-binding domain approximating a consensus IQ element satisfying Thymidine kinase the amino acid pattern IQxxxRGxxxR (Jurado et al., 1999), with x denoting any residue. CaM binding at this IQ domain is critical for CaM/channel regulation (Liu et al., 2010), and mutations in the central isoleucine strongly attenuate Ca2+ regulation (Shen et al., 2006 and Yang et al., 2006). Here, we reveal the existence of ADAR2-mediated RNA editing of the IQ domain of CaV1.3 channels. This editing appears specific to the central nervous system, and proteomic analyses indeed confirm the presence of edited CaV1.3 channel proteins within native brain tissues. Adding to the theme of specificity, no RNA editing was found for CaV1.3 coding regions outside of the IQ domain, nor was IQ-domain editing present in any other members of the CaV1-2 channel family. All these features suggest that CaV1.3 editing may entail distinctive sequelae for the CaM-dependent inactivation (CDI) of these channels, particularly in relation to the availability of these low-voltage activated channels to support neurotransmission at ribbon synapses (Evans and Zamponi, 2006 and Yang et al., 2006) and repetitive firing within neurons throughout the brain (Chan et al., 2007). Accordingly, we demonstrate that RNA editing of the CaV1.

The values of a and x were constrained to a range of 0 – 1. To measure cross-prediction of composite

models between lineages, for each source lineage we examined all 100 combinations of the 10 axial templates and 10 surface templates showing highest predictive power in the source lineage. Each of these 100 combinations was tested by measuring correlation between predicted and observed responses in the independent test lineage. Given two source lineages, this meant a total of 200 candidate models was tested. A significance Selleckchem CHIR99021 threshold of p < 0.05 corrected for 200 comparisons required an actual threshold of p < 0.00025 (Figure S5A). For the models generated from the overall dataset comprising both lineages, we used a two-stage cross validation procedure at a significance threshold of p < 0.005 (Figure S5B). The

higher significance threshold was chosen because the more inclusive model source dataset could generate more accurate GDC-0449 supplier models, and because it is closer to the strict corrected threshold (p < 0.00025) used in the cross-lineage prediction test described above. In the “outer loop” of this procedure, we held out a random 20% of stimuli (from the combined, two-lineage dataset) for final model testing. This was done five times, as is standard in 5-fold, 20% holdout cross-validation. In the “inner loop” of this procedure, we again held out 20%, of the remaining Ketanserin stimuli, for testing the response prediction performance of candidate model templates (which were drawn only from stimuli remaining after both holdouts). This inner loop was also iterated five times (within each iteration of the outer loop). We selected the template with best response prediction

performance on inner loop holdout stimuli, then measured the performance of this template model on the outer loop holdout stimuli. Thus, both model selection and final model testing were based on independent data. The values reported for examples in main text and shown in the Figure S5B distribution are averages across the five outer loop results for each neuron. In applying this procedure to the composite model, each inner loop test of a candidate model required fitting two variables to define the relative weights of the axial, surface, and product terms. Since this fitting was based solely on the inner loop holdout stimuli, the final test on the outer loop holdout stimuli was not subject to overfitting.

1 Hz. To calculate LowFq, each 64–200 Hz power time courses was decomposed into check details nine 60 s blocks, with 30 s overlap of consecutive blocks. First, the mean time course value was subtracted from each 60 s block. Second, each block was multiplied by a 60 s Hamming window. Third, a 600-point DFFT was computed for each block. Fourth, to compute the modulation spectrum of each block, we averaged the power spectra across all blocks in the first and second presentations of the movie. Finally, using this averaged modulation spectrum, we computed LowFq as the power in the modulation spectrum below 0.1 Hz divided by the total power in the modulation spectrum.

Estimations of LowFq in the fixation data were performed in the same way, but using 20 s data windows with 10 s overlap. The ACW was defined as the full-width-at-half-maximum of the temporal autocorrelation function of the power time course. To calculate ACW, each 64–200 Hz power time courses was decomposed into 20 s blocks with 10 s of overlap. We computed the autocorrelation function, Bortezomib molecular weight R  i(τ), of

the power fluctuations of the i-th electrode within each block: Ri(τ)=corr(Pi(t),Pi(t−τ)),Ri(τ)=corr(Pi(t),Pi(t−τ)),and then averaged the Ri(τ) functions across all blocks obtained from all runs within a condition. Finally, the ACW for the i-th electrode was defined as ACWi=2minττ,where R¯i(τ) is the average of all autocorrelation functions Ri(τ) computed within individual blocks for that

electrode. Spectral power was estimated in 1 s windows stepping by 0.1 s, so that τ values increment by 0.1 s and the minimum value of ACW is 0.2 s. The Wiener-Khinchin theorem connects the autocorrelation function Etomidate and power spectrum of a time series, and so the LowFq and the ACW parameters are related measures of the dynamical timescale. In the present data the LowFq and ACW parameters are robustly correlated (Figure S2), but we present both measures because they are differently parameterized (LowFq requires a frequency cutoff while the ACW measure requires an autocorrelation cutoff) and they do not always provide the same information. Because of the autocorrelation in the power modulation time courses, the statistical significance of r-values was assessed using a permutation procedure (Efron and Tibshirani, 1993) that preserved the autocorrelation structure of the original data within the surrogate data. Time courses were subdivided into blocks of 20 s length and the blocks were randomly permuted to produce a surrogate time course. For each empirical time course a set of 2,000 surrogate time courses was generated. For every empirical correlation, 2,000 surrogate correlations were computed using the surrogate time courses. p values were assigned to each r-value by comparing the observed correlation against the distribution of correlations under the null model.

To verify that the small differences in injection site were not responsible for the observed differences in cortical input, we examined the degree of correlation between the anterior-posterior position of the center of the striatal injection site and the anterior-posterior center of gravity of cortical input across all cell types (n = 19). We determined that injection site location predicted less than 5% of the variance in cortical input location (Figure S2). As expected, the cortical center

of gravity for D1R-Cre mice fell below the best-fit line for 7 of 9 animals, whereas cortical center of gravity for D2R-Cre mice fell above the best-fit line for Caspase activation 7 of 10 animals. These observations indicate that cell type identity is much more likely to be the major contributor to cortical input specificity. It is known that two morphologically distinct types of corticostriatal pyramidal cells exist, which have been proposed to differentially innervate striatal projection neuron subtypes (Lei et al., 2004 and Reiner et al., 2003). Intratelencephalic-type (IT-type) pyramidal neurons project to both ipsilateral and contralateral striatum, whereas another type of corticostriatal neuron only projects to ipsilateral striatum but also sends projections along the pyramidal tract (PT-type). There is some evidence to suggest that

these two cell types may preferentially reside in different cortical layers in rats (Lei AZD2281 concentration et al., 2004 and Reiner et al., 2003), although there are also studies in both mice and rats suggesting that PT and IT neurons largely Vasopressin Receptor inhabit the same

cortical layers (McGeorge and Faull, 1987 and Sohur et al., 2012). To determine whether different cortical layers preferentially targeted the direct or indirect pathway, we documented the levels of layer 2/3, superficial layer 5, and deep layer 5 monosynaptic inputs onto either D1R or D2R-expressing MSNs. When examined across the four cortical regions that provided the greatest input to dorsal striatum (Figure 4I), direct- and indirect-pathway MSNs received similar proportional levels of input from each cortical layer (S1, primary somatosensory cortex; M1, primary motor cortex; M2, secondary motor cortex; PFC, insular and orbitofrontal cortices; p > 0.15 for all individual cortical region/layer D1R versus D2R comparisons by two-tailed t test). Furthermore, there was no significant difference in terms of overall cortical input strength from any specific input layer. For layer 2/3, inputs were 19.3% ± 2.5% versus 23.3% ± 2.1% of overall cortical inputs from D1R versus D2R, mean ± 1 SEM, p = 0.2 by two-tailed t test. For superficial layer 5, including all layer 5 input from prefrontal regions, inputs were 56.7% ± 2.6% versus 55.7% ± 2.7%, p = 0.8.