Four juvenile (all male) and six adult (two female) Rhesus macaque monkeys learned to use touchscreens in their home cages to choose quite accurately between pairs of stimuli to select a reward amount (Figures 1C–1F). The two stimuli could be arrays of dots inside a circle or two symbols (Arabic numerals or English letters). Reward amounts corresponded to

the number of dots in a circle or the assigned value of the symbol—numerals Temozolomide in vivo 0 through 9 corresponded to 0 to 9 drops, and the letters X Y W C H U T F K L N R M E A J represented 10 through 25 drops. The monkeys were first trained on 0 versus 1, and each new symbol was introduced, in ascending order, only after the monkey’s choice behavior indicated that he or she had learned the value of the preceding symbol. After 1 year of daily training, during a month-long period while no new symbols were introduced, the monkeys were tested on alternate days with symbol pairs or dot pairs exclusively, with values between 0 and the maximum learned symbol (21 for the juveniles and various lower values for each of the adults). Reaction-time histograms (Figure 2A) for adults and juveniles were similar when they chose between dot arrays (peak of a log 5-FU solubility dmso Gaussian fitted to the distribution = 470 ms for the juveniles; 490 ms for the adults), and reaction times

for juveniles were about the same regardless of whether they chose between symbols (peak = 460 ms) or between dots (470 ms). Adults only, however, were slower specifically when choosing between symbols (peak = 650 ms). One year later, after learning more symbols (up to value 25 for the juveniles and various lower values for each

of the adults), the reaction times of all the monkeys were measured again during another month-long period while no new symbols were introduced. The peak of the fitted reaction time distribution was 490 ms for juveniles using dots, 510 ms for adults using dots, 450 ms for juveniles using symbols, and 630 ms for adults using symbols. Thus the average reaction times were stable, and adults choosing between symbols were slower than adults choosing between dots or juveniles choosing between either symbols or dots. Figure 2B compares the peaks of the fitted reaction from time distributions between dots and symbols for each monkey over the two testing periods. Reaction times were not significantly different between the two testing periods (t(19) = −1.894, p = 0.08, two-tailed t test) so the reaction time distributions from the two test periods were combined to obtain a single peak time for each monkey for statistical comparisons between adults and juveniles and between dots and symbols. The juveniles responded slightly faster to symbols than to dots, but the difference was not significant (t(6) = −0.99, p = 0.36, two-tailed t test), while the adults showed slower reaction times for symbols than for dots (t(10) = 2.66, p = 0.04, one-tailed t test, corrected for multiple comparisons).

As such, both Z scores were non-significant. It is worthy to note that the performance avoidance goal was very close to being significantly different than zero (g = −0.15, Z = −1.91). The review

of the homogeneity statistics found in Table 2 revealed significant heterogeneity distributions for the performance approach (Q = 66.24, p < 0.001) and avoidance goals (Q = 57.46, p < 0.001). A large level of between-study variation existed (I2 = 72.83) for the performance approach goal and a medium level for the performance avoidance goal (I2 = 68.67). Non-significant heterogeneity distribution resulted for both of the mastery goals and performance contrast. Thus, moderator analyses were not conducted. For the performance approach goal (Table 3), significant variation existed between the coded moderator variables for the sample mean age (QB = 12.58, Selleck Trichostatin A p < 0.001), objectivity and subjectivity of the performance measure (QB = 15.88, p < 0.001) and study sex composition (QB = 18.02, p < 0.001). Specifically, for participants that were on average 18 or older, the effect size was moderate (g = 0.47) compared to the small effect for participants on average under 18 (g = 0.20). For the objectivity/subjectivity

moderator variable, the effect sizes were very different with the subjective measures (g = 0.08) being very small compared to the moderate (g = 0.48) effect size for the objective performance measures. For the sex composition of the studies, males (g = 0.46) and mixed (g = 0.44) samples were moderate in effect size compared to the small effect size for females (g = 0.22). For the BI 6727 mw performance avoidance goal, significant differences existed for all of the moderator categories: mean sample age (QB = 26.82, p < 0.001), objectivity/subjectivity of the performance measure (QB = 13.93, p < 0.001), study sex composition (QB = 15.40, p < 0.001), and study setting (QB = 19.30, p < 0.001). Specifically, for mean sample age, participants that were on average 18 or older, the effect size was 0 compared to the small to moderate effect for participants on average under

18 years of age (g = −0.33). For the objectivity/subjectivity of the performance measures, the effect sizes were very similar with the subjective measure (g = −0.42) Levetiracetam being greater in magnitude than the objective measure (g = −0.08). For study sex composition, females (g = 0.19) and mixed (g = −0.25) samples were in opposite direction small in magnitude suggesting that the performance avoidance goal is beneficial for female performance while detrimental in a sample of both sexes. The male effect size was quite small at −0.06. Last, the performance avoidance goal differed significantly based on the setting with the lab setting being motivationally beneficial (g = 0.36) and the naturalistic setting being detrimental to performance (g = −0.23) with the effect sizes in the small to moderate range.

Moreover, recent results suggest that when attention is directed not to a region of space but to a visual feature, variability and correlation decrease in the population that encodes this feature (Cohen and Maunsell, 2011), suggesting that a phenomenon analogous to desynchronization has occurred in a spatially

distributed neuronal assembly. The mechanisms of cortical state change have been a subject of investigation for many decades. Classical research yielded two schools of thought on this question. The first, espoused by Steriade and colleagues, held that cortical states are modulated primarily via the thalamus. In this view, increased AUY-922 ic50 cholinergic input to thalamic relay cells leads to increased tonic firing and thus to a steady glutamatergic drive to cortex that causes desynchronization. The second perspective, espoused by Vanderwolf and colleagues, held that cortical state reflected direct neuromodulation of neocortex. Recent research provides support for both mechanisms. In the rodent somatosensory system, whisking causes increased tonic firing in thalamus; blocking thalamic firing with muscimol reduces the cortical depolarization caused by whisking, whereas stimulating CP-690550 in vitro thalamus optogenetically causes cortical desynchronization (Poulet et al., 2012).

Support for direct cortical neuromodulation comes from the ability of locally applied neuromodulatory blockers to reduce the desynchronization caused by electrical stimulation of nucleus basalis or locomotion (Goard and Dan, 2009 and Polack et al., 2013). If attention does indeed consist of cortical state change occurring at a local level, one might expect the two phenomena to have similar circuit mechanisms. In particular, given the role of

top-down cortical connections in attention, it has been hypothesized that tonic glutamatergic input from higher-order cortex should also cause desynchronization in rodent cortex (Harris and Thiele, 2011). The study of Zagha et al. (2013) provides direct evidence for this hypothesis. Zagha et al. (2013) performed a number of elegant experiments to study the role of top-down connections from vibrissa motor cortex (vM1) to barrel cortex (S1). They found ADAMTS5 that blocking spiking in vM1 using muscimol shifted S1 toward more synchronized states, whereas optogenetically increasing vM1 activity shifted S1 toward more desynchronized states. This desynchronization was usually accompanied by an increase in firing rate of S1 neurons. Importantly, the effects on S1 state did not simply reflect the consequence of these manipulations on behavior. As might be expected, suppression or activation of vM1 activity caused a corresponding decrease or increase in the probability and amplitude of whisking. Nevertheless, an effect of manipulating vM1 on S1 state was seen even when analyzing data within whisking or nonwhisking periods.

Therefore, for values of presynaptic spike amplitude, we used short depolarizing pulses, at which spikes initiate from resting potential and are likely representative of those normally occurring at the contact, averaging 87.6 ± 0.9 mV SEM (n = 203). These

measurements yielded an orthodromic CC of 0.008. The input resistance of the M-cell lateral dendrite was directly measured under single-electrode voltage-clamp configuration during intradendritic recordings (see Experimental Procedures) and found to be, on average, 1.32 ± 0.3 MΩ SEM (n = 9; Figure 5B). The population antidromic CC (M-cell to CE) was calculated as the ratio between the amplitude of the antidromic (AD) coupling potential (the coupling of the antidromic spike of the M-cell in

the CE) and the Ruxolitinib in vitro amplitude MDV3100 concentration of the antidromic M-cell spike (AD spike) recorded in the dendrite (Figure 5C; CC, AD coupling potential/AD spike). Because the AD coupling potential is greatly reduced by electrotonic attenuation when recorded at the VIIIth nerve root during simultaneous recordings, we estimated its average value by performing intraterminal recordings in the vicinity of the M-cell lateral dendrite. This recording position allows measurement of the true amplitude of the AD coupling without the effect of attenuation by electrotonic axonal propagation (Figure 4C, bottom right). The coupling averaged 5.07 ± 0.31 mV SEM (n = 24) but was subsequently corrected to 1.85 ± 0.11 mV SEM to take account for the amplification of the AD coupling produced by a persistent sodium current (INa+P), which is present in these afferents. (The correction was based on a predicted amplification of 63.6% of the average AD coupling amplitude from previous correlations of percent INa+P amplification versus AD coupling amplitude at resting

potential; see Experimental Procedures; Curti and Pereda, 2004.) We next considered the AD spike amplitude that is, on average, most representative unless of that “seen” by the population of CEs. We reasoned that the amplitude of the AD spike at the center of the terminal field of CEs in the lateral dendrite would yield a good approximation. Because the amplitude of the M-cell AD spike decays along the lateral dendrite (the M-cell spike is generated at the axon initial segment and neither the soma nor dendrite have active properties; Furshpan and Furukawa, 1962) and because the precise location of the electrode in the dendrite cannot be controlled, this AD spike amplitude varies between experiments (10–20 mV). Therefore, to estimate the amplitude of the AD spike at the center of the terminal field of CEs, where most CEs terminate (Lin et al., 1983), we performed multiple sequential recordings along the M-cell dendrite (Figure 4D).

Brainstem tissues were maintained for 1 hr at RT in aCSF, bubbled with 95% O2 and 5% CO2, containing Rp-cGMPS (3 μM, dissolved in 0.1% DMSO), PTIO (100 μM, with 0.1% DMSO), or none of them (control, with 0.1% DMSO). After measuring wet weight of individual brainstem PCI-32765 cost tissue samples, they were homogenized in 0.32 M sucrose, 4 mM HEPES-NaOH (pH 7.3 kept at <4°C), supplemented with EDTA-free protease inhibitor cocktail (Roche), phosphatase Inhibitor Cocktail 3 (Sigma), and kinase inhibitor sodium orthovanadate

(Sigma). For lipid extraction, an ice-cold methanol/chloroform (2/1) mixture was added to each pellet. Samples were centrifuged at 1,500 rpm for 5 min, and then supernatants were removed to extract neutral lipids. To extract acidic lipids, an ice-cold methanol/chloroform/HCl 12N (80/40/1) mixture was added to the remaining tissue pellets, vortexed thoroughly, Alectinib and centrifuged at 1,500 rpm for 5 min. Supernatants were collected and a chloroform/0.1N-HCl (1/2) nonpolar/polar solvents mixture was added to create a phase split of liquid to separate lipids from remaining other constituents. The chloroform phase (lower phase) comprising lipids was collected and evaporated by a speed-vacuum centrifuge (Eppendorf Concentrator

5301). Dried lipids samples were then rapidly dissolved again in PBS containing the PIP2 sensor provided in the PIP2 Mass ELISA kit (K-4500, Echelon). PIP2 in each sample was then quantified following the manufacturer’s protocol. Luminometric analyses were performed by measuring the final signal absorbance at 450 nm using a microplate reader (Benchmark Plus 170-6930J1, Carnitine dehydrogenase Bio-Rad). PIP2 levels were normalized to the wet weight of brainstem tissue. Brainstem, heart, and liver tissues of P7 and P14 rats were homogenized in ice-cold buffer containing 50 mM Tris, 1 mM EDTA, 150 mM NaCl, 1% NP-40, and a protease inhibitor cocktail (Roche). Homogenates were then centrifuged at 12,000 rpm for 30 min. The protein concentrations of lysates were measured using Micro BCA Protein assay kit (Thermo Scientific) and equal amount of proteins were loaded onto a SDS polyacrylamide gel. After separation by

electrophoresis proteins were transferred to a PVDF membrane (Bio-Rad). Blotted membranes were blocked with 5% nonfat dry milk in Tris-buffer saline with 0.1% Tween-20. Proteins were detected with the specific primary rabbit antibodies anti-PGKI (Abcam) and anti-alpha-tubulin (Sigma) prior to incubation with horseradish peroxidase-conjugated anti-mouse or anti-rabbit secondary antibody (Millipore). Protein immunoreactivity was further detected using ECL plus western blotting detection reagents (Amersham). Quantitative analyses were performed with a cooled CCD camera coupled with AE- 6971 Light Capture instrument (ATTO) and analyzed with the supplied CS-Analyzer software (ATTO). Data were analyzed using IGOR Pro 4 (WaveMatrics), MS Excel 2003 (Microsoft), and Sigmaplot 11 (Synstat Software) softwares.

However, it remains possible that training only shaped the tuning of a subset of neurons that were most informative for heading discrimination around the straight-forward reference used in training (e.g., Raiguel et al., 2006 and Schoups et al., 2001). If so, then effects might only be

seen for neurons most sensitive to heading variations around straight forward, and may have been missed in the above analysis. To examine AC220 order this further, we interpolated tuning curves and used Fisher information analysis (Gu et al., 2010, see Experimental Procedures) to compute the sensitivity of each neuron for discriminating heading around straight forward. As shown in Figures 4C and 4G, the most sensitive neurons (lowest thresholds) are generally those that prefer lateral headings, such that their tuning curves have a steep slope around straight-ahead. For quantitative analysis, neurons were divided into two groups by heading preference: “fore-aft” neurons with heading preferences within 45° of forward (0°) or backward (±180°) motion, and “lateral” neurons with heading preferences

within 45° of leftward (−90°) or rightward (90°) movements. Consistent with previous findings (Gu et al., 2008a and Gu et al., 2010), Paclitaxel clinical trial lateral neurons were significantly more sensitive than fore-aft neurons for heading discrimination around straight ahead (p << 0.001, Factorial ANOVA, Figures 4D and 4H). However, neuronal sensitivity was not significantly different between naive and trained animals (p > 0.5, factorial ANOVA) for either group of neurons, with no significant interaction effect (p > 0.3). In summary, whereas heading discrimination training clearly

reduced correlated noise among MSTd neurons, we find no clear evidence that training altered the basic tuning properties or sensitivity of individual neurons, including those neurons that are most informative for performing the task. This result also generalizes to neuronal discrimination of heading about any arbitrary reference (Figure S4). It is well established that rnoise is related to rsignal (Cohen and Maunsell, 2009, Cohen and Newsome, 2008, Gutnisky and Dragoi, 2008, Huang and Lisberger, 2009, Kohn and Smith, medroxyprogesterone 2005, Smith and Kohn, 2008 and Zohary et al., 1994b), so it is important to evaluate whether training alters this relationship. Figures 5A and 5B show the relationship between rnoise and rsignal, with each datum corresponding to a pair of MSTd neurons. This relationship was quantified using general linear models (analysis of covariance, ANCOVA), with rsignal in each stimulus condition (visual or vestibular) as a continuous variable and training group (trained or naive) as a categorical factor. There was a significant positive correlation between rnoise and rsignal in both stimulus conditions (vestibular: p = 0.0001; visual: p = 0.

On day 1 mice were habituated to the training chamber for 12 min. Training occurred on day 2 as follows: mice were allowed to acclimate to the chamber for

4 min prior to the onset of six consecuative training blocks, each consisting of a 20 s baseline, followed by a 20 s, 2 KHz, 80 dB tone (conditioned stimulus, CS), followed by an 18 s trace interval of silence, followed by a 2 s scrambled 1 mA foot shock (unconditioned stimulus, US), followed by a 40 s intertrial interval (ITI). On day 3 mice were tested. Mice were first placed in the training chamber for 3 min to assess contextual fear conditioning, after which they were returned to their home cage for 3 min. Testing for trace fear conditioning took place in a novel chamber, which Hydroxychloroquine in vivo was CB-839 concentration distinct from the training chamber. Mice were allowed to acclimate to the novel chamber for 3 min

prior to tone presentation to assess % freezing in the novel chamber. Next, mice were presented with four testing blocks consisting of a 20 s baseline followed by a 20 s 2 KHz, 80 dB tone followed by a 60 s ITI. Percentage of time freezing was quantified using automated motion detection software (CleverSys). Hippocampal neurons from E18 rat pups were plated onto poly-L-lysine coated dishes or coverslips in Neurobasal growth medium supplemented with 2% B27, 2 mM Glutamax, 50 U/mL penicillin, 50 μg/mL streptomycin, and 5% FBS. Neurons were switched to serum-free Neurobasal medium 24 hr postseeding and fed twice a week. Neurons were transfected at DIV 14–15 using lipofectamine 2000 (Invitrogen) and pH-GluA2 recycling live-imaging assays were performed 48 hr posttransfection as described previously (Lin and Huganir, 2007). Briefly, coverslips containing neurons were assembled onto a closed perfusion chamber and continuously perfused with recording

buffer (25 mM HEPES, 120 mM NaCl, 5 mM KCl, 2 mM CaCl2, 2 mM MgCl2, 30 mM D-glucose, 1 μM Thymidine kinase TTX, pH 7.4). After 10 min of baseline recording (F0), neurons were perfused with NMDA solution (25 mM HEPES, 120 mM NaCl, 5 mM KCl, 2 mM CaCl2, 0.3 mM MgCl2, 30 mM D-glucose, 1 μM TTX, 20 μM NMDA, 10 μM glycine, pH 7.4) for 5 min before the perfusion was switched back to recording buffer for the remainder of the session. All imaging experiments were performed at room temperature using a Zeiss LSM 510 Meta/NLO system (Carl Zeiss, Thornwood, NY). The pHluorin fluorescence was imaged at 488 nm excitation and collected through a 505–550 nm filter, while the mCherry signal was imaged at 561 nm excitation and 575–615 nm emission. Neurons were imaged through a 63× oil objective (N.A. = 1.40) at a 3 μm single optical section and collected at a rate of 1 image per min.

, 1999, Kanwisher et al., 1997, Puce et al., 1995 and Schacter and Buckner, 1998). For example, object repetition attenuates activity in the lateral occipital complex (LOC, Grill-Spector et al., 1999 and Buckner et al., 1998), while face repetition and scene repetition attenuation effects are found in the fusiform face area (FFA, Jiang et al., 2006) and the parahippocampal place area (PPA, Epstein et al., 1999), respectively. Perceptual Depsipeptide attention enhances stimulus-specific representations, as measured with fMRI repetition attenuation. An object appearing

in a cued location shows more repetition attenuation than an object appearing in an uncued location (Eger et al., 2004 and Chee and Tan, 2007). In one study, participants were presented on each trial with a face and scene that overlapped spatially and were cued to attend either to the face or the scene. Repetition attenuation was observed in PPA when scenes were repeated

on a subsequent trial only when participants were instructed to attend to the scene on both the first and second presentation (Yi and Chun, 2005). Thus, attention is important for both encoding and expression of learning. Component processes of reflection (Johnson and Hirst, 1993) are the cognitive elements of what is often referred to as controlled/executive processing or working memory www.selleckchem.com/products/pci-32765.html (Baddeley, 1992 and Smith and Jonides, 1999). Refreshing is the act of briefly thinking of, and thereby foregrounding, Adenosine a percept or thought that was activated moments earlier. Rehearsing maintains information (e.g., several verbal items in a phonological loop, Baddeley, 1992), over longer intervals of several seconds. ( Ranganath et al., 2005 make a similar distinction between early and late maintenance processes.) Selectively refreshing an activated representation of a perceptual stimulus that has just disappeared, a thought

that just became active, or an item that is currently in an active rehearsal set boosts the strength of that item relative to other active items, making it the focus of attention ( Cowan, 2001) and giving it a competitive advantage for additional processing. Thus, refreshing and rehearsing, individually and together, constitute reflective attention that selects, maintains, and manipulates the contents of working memory. Reviving representations that are not currently active involves the component processes of reactivating or retrieving. Once revived, these longer-term memory representations can be further extended briefly by refreshing and/or rehearsing them.

Fukushima et al. (2012) are inclined toward the position that the signal arises primarily

from neuronal spiking in the superficial layers of auditory cortex, based on a proximity argument and on a prior study in rodent auditory cortex. This seems to us to be unlikely, given that in the auditory cortices Proteases inhibitor of the awake monkey, the massive weight of both stimulus-evoked and spontaneous firing is in the granular layers compared to the relatively sparse firing seen in the more superficial layers (see e.g., Kajikawa and Schroeder, 2011). Assuming, as the authors do, that high-gamma power is related to multiunit firing, high gamma generated by high-volume firing in the middle layers is likely to overwhelm any generated by the much more sparse firing in supragranular sites. Fukushima et al. (2012) raise a number of logical possibilities regarding underlying causes of structure in ongoing auditory cortical activity, based on a detailed consideration of the relevant anatomical connectivity

patterns between core and higher-order PARP inhibitor cortices and between auditory core and thalamic regions. They also discuss a provocative idea that ongoing activity in auditory cortex represents a playback of recently experienced stimulation. Continuing down this path to longer time scales, it is noteworthy that the dynamical structure of spontaneous activity across the spectrum in auditory cortex bears a remarkable, and likely noncoincidental, resemblance to the 1/f statistics of the natural auditory environment (Garcia-Lazaro et al., 2006). This fits with the idea that the

blueprint for macaque auditory cortex evolved under the pressures of this natural environment and that in ontogeny, individuals’ auditory Florfenicol cortices further tune to the statistics of that same environment (Berkes et al., 2011). It will be interesting to investigate these relationships further and to see how nature and nurture collaborate in this arena. Needless to say, the causes of “spontaneous order” in auditory as well as other cortices are a prime area for future research, as currently there are many more questions than answers. For example, the authors note work by Raichle and colleagues on so-called “resting state” fMRI as evidence that the brain is constantly active, a line of work that has virtually exploded as a means of mapping large-scale brain functional connectivity networks using graph theoretic analyses (Bullmore and Sporns, 2009). To connect the dots, it is interesting to note that this approach is in principle applicable at smaller scales such as those dealt with here, which would in effect represent subsets or nodes in a larger network. This in turn underscores the point (see also below) that it will be important to relate high-gamma to lower-frequency dynamics, extending down to the infraslow ranges that approximate the time frame of hemodynamic oscillations.

, 2002; Chen et al., 2009; Madisen et al., 2010). Mice were housed and handled in accordance with Brown University Institutional Animal Care and Use Committee guidelines. Genotyping, tamoxifen, immunohistochemistry (IHC), antibodies, and cytochrome oxidase (CO) staining are described in Brown et al.

(2009) and Ellisor et al. (2009) and Supplemental Experimental Procedures. Identical exposure settings were used when comparing labeling intensity across the three genotypes. For neuron density analysis, a barrel outline was created based on CO+ staining (“barrel hollow”) and a perimeter was made 15 μm outside the inner outline (“barrel wall”). The area and the number of NeuN-positive objects TSA HDAC in vitro in the barrel hollow and wall regions were determined and analyzed for significance by Student’s t test. For cell size analysis, five thalamic regions from five medial-to-lateral brain sections were assessed. The measure function (Volocity) was used to calculate the perimeter and area of all outlined cell bodies. Generalized estimating equations (log-normal generalized model) were used to compare genotypes with regards to neuronal size. Pairwise comparisons were made using orthogonal contrast statements, with p values adjusted using the

Holm test to maintain family-wise alpha at 0.05. Statistical find more and experimental details are provided in the Supplemental Experimental Procedures. Brain slice preparation, solutions, and recording ever conditions (Agmon and Connors, 1991; Cruikshank et al., 2010, 2012) are provided in detail in the Supplemental Experimental Procedures. Data were collected with Clampex 10.0 and analyses were performed post hoc using Clampfit 10.0. Resting membrane potentials (Rm), input resistances (Rin), membrane time constants (τm), and input capacitances (Cin) were determined as described in the Supplemental Experimental Procedures. Burst properties were characterized by holding the soma at a membrane potential of −60 mV with intracellular current and subsequently

injecting large negative currents. Tonic and single action potential properties were characterized by holding the soma at a membrane potential of −50 mV with intracellular current and injecting suprathreshhold positive current. Single action potential data were obtained by injecting the minimum current needed to elicit an action potential. Afterhyperpolarizations were evoked by injecting a 2 ms suprathreshold positive current. Generalized hierarchical linear modeling was used to test for differential effects of gene deletion. Comparisons by genotype were made using orthogonal linear comparisons. Surgical procedures, recordings, and analysis are described in the Supplemental Experimental Procedures. NeuroNexus probes were used for recording sessions. LFP signals were sampled, filtered, and recorded using a Cheetah Data Acquisition System (NeuraLynx). The probe was lowered 1,600 μm and responses to vibrissa deflections confirmed electrode placement in SI.