A key property of VLPO neurons is that they receive reciprocal inputs from many regions implicated in arousal, including the TMN, dorsal raphe nucleus and adjacent ventral periaqueductal gray matter (vlPAG), parabrachial nucleus, and LC (Chou et al., 2002 and Lu et al., 2006a). Slice recordings of identified VLPO neurons show that they are inhibited by acetylcholine, norepinephrine,

dopamine, and serotonin (Gallopin et al., 2000 and Gallopin et al., 2004). While VLPO cells are not inhibited by histamine, the TMN neurons also contain the mu-opioid peptide endomorphin, which inhibits VLPO neurons (Greco et al., 2008). MnPO neurons receive only sparse inputs from the LC and periaqueductal gray matter and little if any from the dorsal or median BMS-777607 molecular weight raphe nuclei or from the TMN (Saper and Levisohn, 1983). The effects of these inputs on the MnPO sleep-active neurons remain unknown. Because even rats with very large

VLPO lesions still sleep about 50% as much as normal animals, it is likely that the sleep-promoting system in the brain is distributed with components in addition to the VLPO that may contribute to the inhibition of the arousal systems during sleep. These may include other sleep-active neurons in the MnPO and basal forebrain (Modirrousta et al., 2004 and Takahashi et al., 2009), but evidence that these cells promote sleep is lacking. GPCR Compound Library mouse Recent studies on lesions of the striatum and globus pallidus have reported substantial increases in wakefulness and sleep fragmentation (Qiu et al., 2010). The descending projections from both the nucleus accumbens and globus pallidus are largely GABAergic and include the basal forebrain and lateral hypothalamus (Baldo et al., 2004, Kim et al., 1976 and Swanson and Cwan, 1975). In addition, a population of cortical neurons has been described that express

cFos during sleep and are immunoreactive for both nitric oxide and neuropeptide Y (Gerashchenko et al., 2008). However, their role in producing sleep states, or in state switching, remains to be studied. Thus, although FGD2 it is likely that other sleep-promoting neurons participate in the induction and maintenance of sleep, the VLPO neurons appear to play a particularly important role in this process, as VLPO lesions can substantially reduce sleep for months (Lu et al., 2000). Therefore, in our model for behavioral state switching, we will focus on the interactions of the VLPO with wake-promoting systems. After the discovery of REM sleep in the 1950s, its regulation became a major focus of research. Much work has indicated that neurons in the pons play an essential role as REM sleep is disrupted by transections of the pons or large excitotoxic lesions of this region (Jouvet, 1962 and Webster and Jones, 1988). In addition, just prior to and during REM sleep, high-voltage EEG waves occur in the pons, lateral geniculate, and occipital cortex (hence PGO waves) in cats.

3 ± 0.7% [n = 6]; L3P: 96 ± 4% [n = 5]; L5P: 100% [n = 5]). In Figure 1F, we have plotted the number of APs in dependence on the distance from the cell soma. Recordings were done for

each cell type at the laser intensity used for our experiments. To calculate d∗, we pooled these large scanning region calibration maps with maps using a 150 μm perisomatic radius. The resulting cell-type-specific d∗ values were as follows: L2 stellate cell (n = 23), 74.2 μm; L2 pyramidal cell (n = 18), 102.1 μm; L3 pyramidal cell (n = 24), 119.2 μm; and L5/6 pyramidal cell (L5P, n = 31), 109.9 μm (Figure 1F). To determine the functional microcircuitry, mapping of synaptic inputs was performed for the DZNeP supplier main excitatory cells in the superficial MEC, the L2S, the L2P, and the L3P cells (Alonso and Klink, 1993). For the mapping experiments, a hexagonal grid was projected across the different

layers in the entorhinal cortex. Figure 2A depicts the mapping area. The scanning region (L1–L6) was grouped into the following cortical layers: superficial layers (L1–L4, black) and deep layers (L5–L6, purple; Figure 2A). The two cell types in L2 were identified click here by their characteristic morphological and electrophysiological properties. The mapped cells could be categorized based on their biophysical properties (larger hyperpolarizing and depolarizing sag current and early firing upon depolarization for L2Ss, small hyperpolarizing and depolarizing sag current and slow ramp current with much long-latency AP firing upon depolarization for L2Ps; Figures 2B and 2C left and middle; Alonso and Klink, 1993). The L3P were easily identified based on their laminar location and uniform distribution in layer 3 (Figure 2D). Uncaging of glutamate evoked both direct and indirect synaptic responses. These were clearly

separated by their different delay-to-onset times. Direct responses were elicited almost immediately (in a time window of 10 ms), whereas synaptic inputs were collected up to 95 ms following ultraviolet (UV) photolysis (see Figures 2B and 2C, right; see Bendels et al., 2008 for details). In order to discriminate between photo-induced synaptic input points and background activity, we used a spatial-correlation-based algorithm to extract presynaptic input locations that were termed synaptic points. (Bendels et al., 2010; for details see Experimental Procedures). Further, to exclude the effect of dendritic filtering biasing the detection of somatic EPSCs by the automatic detection algorithm, we applied localized puffs of sucrose at distinct distal dendritic locations in L2Ps and L2Ss. The algorithm was able to faithfully detect EPSCs from distal dendritic locations (for details see Supplemental Experimental Procedures and Figure S1, available online).

, 2013). Size-invariant time parsing in neural networks strongly depends on neuronal conduction velocity. As an example, for gamma oscillation to be synchronous in both hemispheres of the mouse brain, at an interhemispheric distance of ∼5–10 mm, a conduction velocity of 5 m/s is sufficient (Buzsáki et al., 2003). Maintaining coherent oscillations at the same frequency in the human brain, with a 70–140 mm interhemispheric distance (Varela et al., 2001), requires much find more more

rapidly conducting axons. Of the various structural-anatomical possibilities, evolutionary adaptation of axon size and myelination appear to be most critical for a brain-size-invariant scaling of network oscillations because they both determine the conduction velocity of neurons. The benefits of increased brain size should therefore be offset by the cost of larger-caliber axons (Figure 3; Aboitiz et al., 2003 and Wang et al., 2008) so that signals can travel longer distances within approximately the same time window. The scaling laws of axons support this hypothesis. Indeed, axon calibers in the brain vary by several orders of magnitude (Swadlow, 2000). An important evolutionary strategy is the myelination of axons and saltatory conduction; the speed of conduction along a myelinated axon scales relatively linearly with axon diameter (Hursh, 1939 and Tasaki, 1939). In humans, the

great majority of callosal axons, which connect approximately 2%–3% of cortical neurons, have diameters <0.8 μm, but the thickest 0.1% of axons can exceed 10 μm in diameter (Aboitiz http://www.selleckchem.com/products/PLX-4720.html et al., 2003). The calibers of axons emanating from the same neurons but targeting different brain regions can vary

substantially, exemplifying a complex system of lines of communication with different geometrical and time-computing properties (Innocenti et al., 2013). However, a proportional increase of PI-1840 axon caliber in larger brains would enormously increase brain size. Instead, a minority of axons with a disproportionally increased diameter might be responsible for keeping the timing relatively constant across species. Indeed, it is the thickest diameter tail of the distribution that scales best with brain size (Figure 3), whereas across species the fraction of thinner fibers/total numbers of cortical neurons decreases (Swadlow, 2000, Wang et al., 2008, Olivares et al., 2001 and Aboitiz et al., 2003). Although adding a small fraction of giant axons to the neuropil still demands increased volume and an increasing share of the white matter in larger brains, the metabolic costs and the needed volume are still orders of magnitude less than would result from the proportional increase of axon calibers of all neurons. Adding a very small fraction of very-large-diameter axons might guarantee that the cross-brain conduction times increase only modestly (Figure 3B) across species (Wang et al., 2008). The host neurons of the giant axons still need to be identified.

, 2011). The study used

a discovery sample of 353 cases and 366 controls to detect, at genome-wide significance, an association between MD and a marker next to the SLC6A15 gene ( Kohli et al., 2011). Without further replication, the status of this finding is dubious and is likely to be a false positive. While Table 1 only includes GWASs of MD, there are also a number of studies of phenotypes that are genetically related to MD, such as the personality trait of neuroticism (Kendler et al., 1993 and Shifman et al., 2008) or depressive symptoms (Foley et al., 2001 and Hek et al., 2013). These studies are also negative. The largest is a study of depressive symptoms in 34,549 individuals that reports one, unreplicated, p value of 4.78 × 10−8. Overall, we can conclude that no study has robustly identified a locus check details that exceeds genome-wide significance for MD or genetically related traits. We can also conclude that GWAS results have set some constraints on the effect sizes likely to operate at common variants

contributing to susceptibility to MD. Candidate SP600125 in vitro gene studies of MD have generated many publications but few robust findings. At the time of writing (2013), searching for articles dealing with genetic association and MD returned more than 1,500 hits. Almost 200 genes have been subject to testing, many by multiple groups (Bosker et al., 2011 and López-León et al., 2008). The difficulty, common in this area of research, is that few groups agree with each other. Resolution of conflicting results is usually attempted through meta-analysis and Table 2 summarizes data for 26 genes analyzed by meta-analysis, of which seven yield a significant (p < 0.05) result: 5HTTP/SLC6A4, APOE, DRD4, GNB3, HTR1A,

MTHFR, and SLC6A3. We can use the results from Table 1 to interpret the results presented in Table 2. First, we note that the mean effect size (expressed as an odds ratio) across the studies that report a significant effect is 1.35. Second, all of the variants tested, whether significant or not, are common; none have an MAF less than 10%, and the mean is 38% (column headed MAF in Table 2). This means that the results of GWAS are relevant (recall that GWAS interrogates common variants). Virtually all of the candidate variants should DOK2 be detectable by the published GWAS, particularly if imputation is used to obtain data from markers not present on the arrays (Howie et al., 2009) (Figure 1). The fact that the candidate variants do not occur in Table 1 suggests that the results in Table 2 are false positives (recall that the largest published GWAS has greater than 80% power to detect an odds ratio greater than 1.2). Most GWASs include a section reporting the analysis of variants in candidate genes, and by providing a much larger sample size than almost any of the meta-analyses listed in Table 2, their findings are likely to be more robust than the meta-analyses.

The second problem has been the averaging of responses over several distinct cell classes. We know that cortex comprises many different cell types (Connors and Gutnick, 1990, Markram et al., 2004 and Peters and Jones, 1984), which mediate different functions within circuits. One means of distinguishing cell classes is by the shapes of their extracellularly recorded spikes (Barthó et al., 2004, Mitchell et al., 2007 and Niell and Stryker, 2008). Data

indicate that neurons that generate narrow spikes correspond primarily to fast-spiking inhibitory cells, whereas broad-spiking neurons correspond primarily to excitatory pyramidal cells (Barthó et al., 2004, Henze et al., 2000, Kawaguchi and Kubota, 1997, ZD1839 McCormick et al., 1985 and Nowak et al., 2003). No studies to date, however, have probed the potential differential effect of visual experience on distinct cell classes in ITC. Here, we show that experience caused putative excitatory neurons to respond much more robustly to their best familiar compared to their best novel stimuli. In contrast, familiarity caused a dramatic decrease in the maximum and average rates of putative inhibitory neurons. Together, the results suggest that visual experience can profoundly alter visual object representations in ITC. To understand how

long-term sensory input sculpts the responses of individual ITC neurons, we first familiarized selleck inhibitor each of two monkeys with 125 color images of real-world objects (Hemera Photo-Objects: Vol. 1, 2, and 3) (see Figure S1A available online). The monkeys were trained to both passively G protein-coupled receptor kinase fixate the stimuli and to perform a short-term memory task with them. This exposure phase lasted between 3 months (monkey I) and 12 months (monkey D), resulting in an estimated number of exposures equal to 1,000 (monkey I) and 3,000

(monkey D) repetitions per image, split roughly evenly between the two tasks. Once familiarization was completed, we recorded the activity of well-isolated single units in ITC (n = 50 from monkey D; n = 38 from monkey I) in a passive fixation task (Figure 1A). Each neuron was screened with 125 familiar and 125 novel stimuli. The 125 novel stimuli were picked randomly on a daily basis from the same database as the familiar set (for examples, see Figures S1B–S1D). We recorded all units deemed visual by inspection of online stimulus-locked rastergrams. Both monkeys provided qualitatively similar data, so the results have been combined across subjects. Any notable differences are acknowledged (see Figure S3 for the main results split by monkey). As a means of correlating visual response properties with specific cell classes, we characterized the recorded sample of single units by the trough-to-peak widths of their extracellular spike waveforms (Figures 1B and 1C). Consistent with previous studies (Diester and Nieder, 2008, Hussar and Pasternak, 2009 and Mitchell et al.

These studies indicate a segregation of—potentially autonomous—supragranular and infragranular dynamics. Maier et al. (2010) found that supragranular sites had higher broadband gamma power than infragranular MK-8776 datasheet sites. This pattern was reversed in the alpha and beta

range, with greater power in the infragranular and granular layers. Finally, the spiking activity of neurons in the superficial layers of visual cortex are more coherent with gamma-frequency oscillations in the local field potential, while neurons in deep layers are more coherent with alpha-frequency oscillations (Buffalo et al., 2011). This finding is consistent with an earlier study by Livingstone (1996) showing that 50% of cells in L2/3 of squirrel monkey V1 expressed gamma oscillations, compared to less than 20% of cells

in L4C and infragranular layers. The different spectral behavior of superficial and deep layers has led to the interesting proposal that feedforward and feedback signaling may be mediated by distinct (high and low) frequencies (reviewed in Wang, 2010; see also Buschman and Miller, 2007), a proposal that has recently received experimental support, at least for the feedforward connections (Bosman et al., 2012; see also Gregoriou et al., 2009). Given this functional and anatomical segregation into parallel streams, the question naturally arises, how are these streams integrated? It has been previously suggested that integration occurs through the synchronized firing of multiple neurons that PS341 form a neural ensemble (Gray et al., 1989; Singer, 1999), while others have emphasized interareal phase synchronization or coherence (Varela et al., 2001; Fries, 2005; Fujisawa and Buzsáki, 2011). While a full treatment of this

question is beyond the scope of the current Perspective, we propose that the canonical microcircuit contains a clue for how the dialectic between segregation and integration might be resolved. While top-down and bottom-up inputs and outputs may be segregated in layers, streams, and frequency bands, the canonical microcircuit specifies the circuitry for how the basic units of cortex are interconnected and therefore how the intrinsic activity of the cortical column is entrained by extrinsic inputs. This intrinsic connectivity specifies how the cells of origin Evodiamine and termination of extrinsic projections are interconnected and thus determines how top-down and bottom-up streams are integrated within each cortical column. The notion of a canonical microcircuit implicitly assumes that each circuit is distinct from its neighbors, which could presumably carry out computations in parallel. Therefore, the canonical microcircuit specifies the spatial scale over which processing is integrated. The most likely candidate for this spatial scale is the cortical column, which can vary over three orders of magnitude between minicolumns, columns, and hypercolumns.

All behavioral tests were conducted > 5 weeks postsurgery. The tests are described in the order in which they were performed. For all test sessions, the start of a session was indicated to the rat by the illumination of a white house light and the onset of low-volume white noise (65 dB) to mask extraneous sounds. Peak light output during photostimulation was estimated to be ∼1.5–2 mW at the tip of the implanted fiber for each session, and ∼0.45–0.6 mW/mm2 at the targeted tissue 500 μm from the fiber tip. This peak light power was based on measuring the average light power for the pulsed Bcl-2 inhibitor clinical trial light parameters used during experiments (20 Hz, 5 ms duration), and then correcting for the duty

cycle to arrive at the peak power (in this case by dividing by 0.1). The power density estimate was based on the light transmission

calculator at www.optogenetics.org/calc. During the first training session, both active and inactive nosepoke ports were baited with a crushed cereal treat to facilitate initial investigation. Rats were given four daily sessions of two hours each in which they could respond freely at either nosepoke port. For all rats (Th::Cre+ and Th::Cre−), a response at the active port resulted in the delivery of a 1 s train of light pulses (20 Hz, 20 pulses, 5 ms duration). Concurrently, the LED lights in the recess of the active port were illuminated, providing a visible cue whenever stimulation was delivered. Responses at the active port made during the 1 s period when the PF 2341066 light train was being delivered were recorded but had no consequence. Responses at the inactive port were always without consequence. The duration-response Fossariinae test measured the rats’ response to stimulation trains that varied systematically in length. As before, all stimulation trains consisted of pulses of 20 Hz frequency and 5 ms duration. The test was organized into nine trials, and in each trial nosepokes at the active port were rewarded with stimulation trains

of a specific length (100, 80, 60, 40, 20, 10, 5, 3, or 1 pulse/train). The first trial consisted of the longest stimulation length (100 pulses); the next trial consisted of the next longest stimulation length (80 pulses), and so on in descending order. A series of all nine trials was considered to be a “sweep.” A session consisted of four consecutive sweeps. The data presented is an average of all eight sweeps from two consecutive days of testing. The start of a trial was signaled by the illumination of the house light and the onset of low-level white noise as described above. Three “priming” trains of stimulation were then delivered noncontingently to inform the rat of the stimulation parameters that would be available on the upcoming trial. The separation between these trains was equal to the length of stimulation or 1 s, whichever longer.

Neural sensitivity to incentive was defined as the slope of the relationship between BOLD percent signal change

and incentive level; a positive neural sensitivity corresponded to neural activation, whereas a negative activity was indicative of deactivation. In keeping with the first prediction, we found significant correlations between levels of striatal deactivation at the time of the motor task and performance decrements at the $100 incentive level (Figure 4B; r = 0.70; p = 0.001). Critically, no significant relationship between neural sensitivity and performance was found at the time of incentive presentation (r = 0.22; p = 0.38). Using LGK-974 cost a cross-product term in a multiple regression model, we also found a significant interaction between neural sensitivity during incentive presentation and the motor task and performance (statistics for interaction term: t(14) = 4.18; p = 0.001). To test the second prediction we recalled a subset of participants

(n = 12) who originally participated in these experiments and tested them on a behavioral loss aversion task. This task was the same as that used by Tom et al. (2007), and allowed us to determine a measure λ, indicating how heavily participants weighed losses compared to gains. This subset of participants was found to have a median λ estimate of 2.09 (interquartile range [IQR] 1.09). These values of λ are similar to those ERK inhibitor screening library reported in previous studies (Bateman et al., 2005, Gachter et al., 2007, Tom et al., 2007 and Tverskey and Kahneman,

1992). We found significant correlations between increasing behavioral loss aversion and striatal deactivation during motor action (Figure 5A; r = 0.60; p = 0.04; Figure S3). Importantly, we did not find a significant correlation between neural sensitivity during incentive until presentation and participants’ behavioral loss aversion (r = 0.30; p = 0.34). We also found a significant interaction between neural sensitivity during incentive presentation and the motor task and loss aversion (statistics for interaction term: t(8) = 2.40 p = 0.05). These results illustrate that differences in behavioral loss aversion were indicative of neural responses during motor action. To test the third prediction, and to reach an adequate sample size to test behavioral correlations, we included an additional 20 participants who performed the motor task, the behavioral loss aversion task, and a risk aversion task outside the fMRI scanner. A group comprised of both the subset of imaging participants (n = 12), and the additional participants (n = 20) had a median λ estimate of 2.10 (IQR 0.85). We found a highly significant (r = 0.53; p = 0.002) relationship between increasing behavioral loss aversion and the proclivity to show performance decrements in the hard difficulty level ( Figure 5B), but not in the easy difficulty level (r = 0.22; p = 0.23). We also found a significant relationship (r = 0.

Similarities between remembering past events and

imagining future events had also been documented in a study of depressed patients (Williams DAPT et al., 1996) as well as in behavioral studies of healthy individuals (e.g., D’Argembeau and Van der Linden, 2004, 2006; Spreng and Levine, 2006; Suddendorf and Busby, 2005), and were explored in experiments that investigated whether non-human animals can project into the past or future (e.g., Clayton and Dickinson, 1998; Emery and Clayton, 2001). Social psychologists had published studies concerning the role of mental simulations in predicting future experiences and the role of memory in guiding such simulations (e.g., Morewedge et al., 2005). Moreover, several review papers had discussed relevant theoretical and conceptual issues (Atance and O’Neill, 2001, 2005; Clayton et al., 2003; Ingvar, 1979, 1985; Suddendorf and Corballis, 1997; Tulving, 1985, 2002a, 2002b, 2005; Wheeler et al., 1997). Building on these foundational studies and analyses, the papers published in 2007 served to galvanize scientific interest in the relations between remembering the past and imagining the future, as evidenced by the rapidly growing number of papers on the topic that

have been published since. The main purpose of the present article is to review some of the progress that has been made since 2007 (our review will focus exclusively on studies with human subjects, but relevant recent work has also been conducted with nonhuman animals; for reviews, see Cheke and Clayton, 2010; Crystal, 2012; Roberts, XAV-939 concentration 2012; van der Meer et al., 2012). Specifically, we have organized the literature with respect to four key points that have emerged from research reported during the past five years: (1) it is important to distinguish between temporal and nontemporal factors when conceptualizing processes

involved in remembering the past and imagining the future; (2) despite impressive similarities between remembering the past and imagining the future, theoretically important differences have also emerged; (3) the component to processes that comprise the default network supporting memory-based simulations are beginning to be identified; and (4) this network can couple flexibly with other networks to support complex goal-directed simulations. We will conclude by considering briefly several other emerging points that will be important to expand on in future research. Note that although the focus of our review will be to elucidate recent advances in understanding the neural mechanisms of memory-based simulations, numerous purely behavioral studies have also shed light on the topic and we will consider those data where appropriate. Throughout the review, we will use the concepts of imagination or “imagining the future” and simulation or “simulating the future” in a roughly interchangeable manner. Schacter et al. (2008; p.

We scaled the excitatory synaptic amplitude by a factor of 0.8–1.2, while keeping the inhibitory response amplitude unchanged (see Figure 4E). Figure 5F shows the frequency tuning curves of peak Vm responses at different excitatory scaling factors. To derive spiking response HIF-1�� pathway from the peak Vm response, we utilized a power-law function in describing the relation between Vm and spike rate (Atallah et al., 2012, Liu et al., 2011, Miller and Troyer, 2002 and Priebe, 2008) (see Experimental Procedures). As shown in Figure 5G, the

scaling of excitatory response amplitudes resulted in negligible changes in the shape of spike tuning, although the spike rate could be modulated by as much as 50%. Within the experimentally observed range of changes of spike rate (0.4- to 1.4-fold, see Figure 1D), excitation was scaled within a range of 0.78- to 1.12-fold, and spike tuning width only varied between a narrow range of 0.93- to 1.03-fold (Figure 5H). Similar, as previously reported (Atallah et al., 2012), scaling of inhibition can also achieve an approximate gain control of spike responses (Figure 5I). The gain modulation by scaling excitation was not affected much by the inhibitory tuning shape, as similar effects on spike tuning were achieved under inhibition cotuned with excitation, more broadly tuned than excitation, or inhibition with

a flat tuning (Figure 5J). Previous studies have demonstrated that the amplitude of binaural spike response

can be modulated by interaural Pomalidomide in vitro level/intensity difference (ILD), a spatial location cue (Irvine and Gago, 1990, Kuwada et al., 1997, Li et al., 2010, Pollak, 2012, Semple and Kitzes, 1985 and Wenstrup et al., 1988). In the experiments described thus far, ILD was set as zero to simulate a sound source originating on the auditory midline. To test whether a linear transformation of the contralateral into binaural spike response also applies to other binaural hearing conditions, we varied ILD to simulate different TCL sound source locations. As shown by an example cell in Figure 6A, the binaural TRFs at several different ILDs all resembled the TRF under contralateral stimulation alone. At each ILD tested, a strong linear correlation between binaural and contralateral spike responses was observed (Figures 6B and 6C). Noticeably, the gain value decreased as ILD became increasingly ipsilaterally dominant, suggesting the progressively increasing influence of ipsilaterally mediated suppression at more ipsilaterally dominant ILDs (Figure 6C). In a total of 24 similarly recorded neurons, except for two cells exhibiting enhancement, the majority of cells showed a reduction of binaural spike response with decreasing ILD (Figure 6C). The linear correlation between binaural and contralateral spike responses was similarly strong (r close to 1) at all testing ILDs and in all the cells examined ( Figure 6E), indicating that gain modulation is a general phenomenon.