“
“When passing the ball to click here a player of his team, a soccer player can identify and select the proper target among many potential targets by the color of the jerseys. In this situation the physical targets are identical to potential targets of action (Figure 1A, left). However, when a striker is approaching the opponent goal, multiple alternative action goals have to be inferred from a single physical target (the goal keeper) via spatial transformation rules (Figure 1A, right). The striker might want to aim for the goal keeper, speculating that he or she will jump away, or for the opposite corner of the goal, hoping that the keeper stays. Recently,

a lot has been learned on how primates represent and decide between multiple physical targets in target-selection tasks, and how different frontal and parietal cortical areas contribute to target valuation and selection (Sugrue et al., 2005, Gold and Shadlen, 2007, Churchland et al., 2008, Rangel et al., 2008, Andersen and Cui, 2009, Kable and Glimcher, 2009, Kim and Basso, 2010, Bisley and Goldberg, 2010 and Cisek PLX4032 purchase and Kalaska, 2010). Little is known, however, about decision processes in rule-selection tasks, which require choosing among goals based on a spatial transformation rule (Tremblay et al., 2002), and in which alternative

goals might not be physically present as target stimuli, but have to be spatially inferred, like in the example of the striker. In rule-selection experiments, alternative movements are conducted under identical spatial sensory conditions, but according to different context-defined transformation rules (Wise et al., 1996 and Wallis and Miller, 2003). In antisaccade or antireach tasks (Figure 1A, right) a single visuospatial input is associated with two alternative movement goals: one that is directly cued by the sensory input (aim at the keeper), and another that has to be inferred from Electron transport chain a spatial cue by applying a remapping rule (aim at the corner of the soccer goal opposite to the keeper) (Crammond and Kalaska, 1994, Shen and Alexander, 1997, Schlag-Rey et al., 1997, Everling et al.,

1999, Zhang and Barash, 2004, Medendorp et al., 2005 and Gail and Andersen, 2006). Two alternative decision processes are conceivable in such rule-selection tasks. The sensorimotor system could first choose among the alternative rules, and then only compute one sensorimotor transformation to encode the single motor goal that is associated with the selected rule (rule-selection hypothesis). Alternatively, the system could first compute all potential sensorimotor transformations, and then select among the multiple resulting motor-goal options (goal-selection hypothesis). The difference between the rule- and goal-selection hypotheses should become obvious in areas of the brain that have “spatial competence” for movement planning, i.e., areas that exhibit spatially selective neural encoding of motor goal information.

We will explain each factor in turn, linking both to spike initiation dynamics. According to our neuron-centric definition of operating mode, integrators can summate asynchronous inputs, whereas Trichostatin A coincidence detectors are excited uniquely by synchronous inputs (see Figure 1). In other words, coincidence detectors are selective for (i.e., tuned to) synchrony, whereas integrators are relatively untuned with respect to synchrony. Synchrony is reflected in spectral properties of the input: synchronous input has greater power

at high frequencies and less power at low frequencies compared with asynchronous input of equivalent magnitude (i.e., with equivalent total power) (Destexhe et al., 2001). Putting two and two together, one might (correctly) postulate that integrators are tuned to lower frequencies, akin to a low-pass filter, whereas coincidence detectors are tuned to higher frequencies, akin to a high-pass filter, although the end result is a band-pass filter when the high-pass filter implemented by spike initiation is combined with the low-pass filter implemented by membrane capacitance. Differential

tuning reflects differences in neuronal excitability. A simple yet invaluable classification of excitability was provided by Hodgkin (1948) who identified three spiking patterns in response to sustained depolarization: Class 1 neurons can spike repetitively at an arbitrarily low rate and thus Linifanib (ABT-869) have a continuous frequency-current (f-I) curve, class 2 DZNeP molecular weight neurons cannot spike repetitively below a certain rate and thus have a discontinuous f-I curve, and class 3 neurons fire only one or a few spikes at stimulus onset ( Figure 4A). Each class of excitability is associated with differences in other response measures such as

the phase response curve ( Ermentrout, 1996) and spike-triggered average ( Ermentrout et al., 2007; Mato and Samengo, 2008) (see below). In general, class 1 neurons exhibit integrator traits, whereas class 3 neurons and, to a lesser extent, class 2 neurons exhibit coincidence detector traits. Hodgkin’s classification thus provides a useful starting point for relating neuronal excitability with operating mode. Differences in excitability reflect differences in spike initiation dynamics (Izhikevich, 2007; Prescott et al., 2008a; Rinzel and Ermentrout, 1998). “Dynamics” refers to how fast and slow currents interact to control spike initiation. Notably, currents with similar kinetics sum linearly whereas those with different kinetics interact nonlinearly. Therefore, net-fast and net-slow currents interact nonlinearly, and ultra-slow processes like adaptation currents or cumulative inactivation of sodium current can be treated as modulating the fast-slow interaction. Net-fast current is necessarily inward (depolarizing) at spike threshold.

After incubation with the first antibody, sections were washed with 1× PBS three times for 20 min each, followed by incubation with Alexa 488-conjugated goat anti-rabbit secondary antibody (Invitrogen) for 2–4 hr at room temperature and then washed with 1× PBS. Talazoparib Sections were transferred onto slides, mounted with 0.1% paraphenylinediamine in 90% glycerol/PBS, and imaged with a microscope (BX61, Olympus). Acute

slices were prepared according to published procedures (Peça et al., 2011). Briefly, mice were anesthetized with Avertin solution (20 mg/ml, 0.5 mg/g body weight) and perfused through the heart with 20 ml of ice-cold oxygenated (95% O2, 5% CO2) cutting solution containing 105 mM NMDG, 105 mM HCl, 2.5 mM KCl, 1.2 mM NaH2PO4, 26 mM NaHCO3, 25 mM glucose, 10 mM MgSO4, 0.5 mM CaCl2, 5 mM L-ascorbic acid, 3 mM sodium tyruvate, and 2 mM thiourea (pH was 7.4, with osmolarity of 295–305 mOsm). The brains were rapidly removed and placed in ice-cold oxygenated cutting solution. Coronal or transverse hippocampal slices (300 μm) were prepared using a slicer (Vibratome 1000 Plus, Leica Microsystems) and then transferred to an incubation chamber (BSK4, Scientific System Design) at 32°C with carbogenated cutting solution, which Bortezomib was gradually replaced with artificial cerebral spinal fluid (ACSF) in 30 min through a peristaltic

pump (Dynamax Model RP-1; Rainin Instruments), allowing a precise regulation of fluid flow rates. The slices were then kept in PIK3C2G the ACSF that contained 119 mM NaCl, 2.3 mM KCl, 1.0 mM NaH2PO4, 26 mM NaHCO3, 11 mM glucose, 1.3 mM MgSO4, and 2.5 mM CaCl2 (pH was adjusted to 7.4 with HCl, with osmolarity of 295–305 mOsm) at room temperature for at least 30 min. Recordings were performed in oxygenated ACSF. Intracellular solution consisted of 130 mM KMeSO3, 10 mM HEPES, 4 mM MgCl2, 4 mM Na2ATP, 0.4 mM NaGTP, 10 mM Na-phosphocreatine,

and 3 mM Na-L-ascorbate; pH was adjusted to 7.3 with KOH. Recordings were performed at room temperature in ACSF. To evoke APs, we held cells in the current-clamp configuration, and we injected 3–5 nA of current for 2 ms through the recording electrode. Cells were selected if their GCaMP fluorescence was homogeneously distributed in the cytoplasm. Fluorescent signals were imaged by a confocal microscope (Fluoview FV 1000; Olympus) with a 30 mW multiline argon laser, at 5%–10% laser power. The laser with a wavelength of 488 nm was used for excitation, and fluorescence was recorded through a band-pass filter (505–525 nm). The images were acquired using 40× water-immersion objectives (NA = 0.8) with 5 Hz scanning speed. XYT image galleries were collected and average fluorescence intensity in the soma was measured for the quantification by Fluoview data processing software.

ensembl.org, http://www.genome.ucsc.edu), and efforts are underway to sequence the epigenome to create DNA methylation and histone modification Onalespib manufacturer maps for as many different cell types as possible (Nature, 2010). There has

also been a surge in research investigating epigenetic mechanisms in the nervous system with a significant literature on memory and synaptic plasticity (for review, see Guan et al., 2009, Peleg et al., 2010 and Day and Sweatt, 2011) and the emergence of a whole new field dubbed “behavioral epigenetics” (Szyf and Meaney, 2008 and Weaver et al., 2004). In chronic pain, three main areas of epigenetic control can be identified based on the work to date and will be discussed below. As explained previously, the importance of inflammatory mediators in the establishment of many pain conditions is well recognized. Equally, there is quite a thorough literature on epigenetic influences in the inflammatory process (for review, see Selvi et al., 2010). Histone deacetylase (HDAC) inhibitors—compounds that prevent the removal of acetyl groups from histones—can ameliorate symptoms in a number of animal models of inflammatory diseases, such as arthritis, colitis, and hepatitis (Chung

et al., 2003, Glauben et al., 2006 and Leoni et al., 2005). Moreover, significant clinical benefits of an HDAC inhibitor have been observed against both arthritic and painful components of juvenile idiopathic arthritis, albeit in an open-label trial (Vojinovic et al., 2011). The effects of these compounds HDAC inhibitor drugs are believed to be mediated in part through suppression of cytokines, with their administration having been shown to reduce expression of many crucial proinflammatory mediators,

including IL-1β and TNFα (Leoni et al., 2002). In turn, binding of these same proinflammatory factors to their receptors can also harness epigenetic processes. Thus, interleukin and TNFα receptor activation results in H4 hyperacetylation of many other inflammatory Resminostat promoters through the action of the transcription factor NF-κB and its subunits p50 and p65 (Ito et al., 2000 and Rahman et al., 2002). Similarly, H3k4 methylation via methyltransferase SET7/9 can affect recruitment of NF-κB to proinflammatory genes (Li et al., 2008). The peripheral mechanisms underpinning chronic inflammatory pain states are controlled by these same mediators (Marchand et al., 2005) and involve action of both glial and neuronal NF-κB (Fu et al., 2010 and Niederberger and Geisslinger, 2008), making it likely that similar epigenetic processes are at play. Three epigenetic factors have so far been uncovered that can influence expression of nociceptive genes in chronic pain states. These are histone acetylation, DNA methylation, and REST. Pharmacological interference with the process of histone acetylation can affect pain behavior, with both systemic and intrathecal administration of HDAC inhibitors having analgesic effects in models of inflammatory pain (Chiechio et al.

, 1997, 1999). Moreover, social defeat has ethological validity, in that it mimics some of the physiological and anhedonic aspects of depression in humans. This study focused on the hippocampus, as postmortem studies pointed to this brain

region as the most altered by MDD (unpublished observations). Moreover, this is an area that is critical in the biology of “stress-related disorders,” including MDD, anxiety, and posttraumatic stress disorder (PTSD). For example, human brain imaging studies have shown that the volume of the human hippocampus is negatively correlated with Selleck BIBW2992 PTSD (Gilbertson et al., 2002), consistent with the view that this area is highly responsive to stress-related disorders. This work, in fact, suggested

that hippocampal volume may be a predisposing factor in PTSD. Thus, the hippocampal size of the twin who had not been exposed to combat predicted the magnitude of PTSD in the combat twin. Since FGF2 can control the development and size of the hippocampus (Ohkubo et al., 2004), it was logical to assess FGF2 expression in this region follow a social stress animal model. It should also be mentioned that neuroimaging studies have shown a reduced hippocampal volume in depressed subjects (Campbell et al., 2004). However, it remains unclear whether this is a result of stress and an antecedent to depression or a consequence of having the disorder. Following repeated social defeat stress, the expression selleck of FGF2, as well as one of its receptors, FGFR1, was decreased in the hippocampus (Turner et al., 2008a), suggesting the hypothesis that the observed decrease in FGF2 both in human MDDs ADP ribosylation factor and an animal model may contribute to the affective changes accompanying depression. Could FGF2 be “an endogenous antidepressant,”

and could its suppression, therefore, contribute to the negative affect of depressed humans? This hypothesis was tested by administering FGF2 intracerebroventricularly to adult rats to ascertain its potential antidepressant-like effects. Following both acute and chronic administration and across multiple tests of depression-like behavior, such as the forced swim test and novelty-suppressed feeding, FGF2 proved to have antidepressant properties (Turner et al., 2008c). Surprisingly, FGFR1 expression was also increased by the FGF2 treatment. This suggested that FGF2 can prime its own receptor and further amplify the effects of its administration. Moreover, other ligands known to bind to and activate FGF receptors, such as neural cell adhesion molecule (NCAM), also decreased depression-like behavior following acute intracerebroventricular administration (Turner et al., 2008c).

Tufted cells, a cell type that we did not target in the current study, are more abundant than MCs (Shepherd et al., 2004), and could carry information on odor identity. Middle tufted cells respond to odors and local processing of the odorant signal in the middle tufted cells differs from that in MCs (Griff et al., 2008 and Nagayama et al., 2004). In addition, external tufted cells whose cell bodies lie adjacent to glomeruli could transmit information on odor Vorinostat order identity (Wachowiak and Shipley, 2006), although

whether these cells can carry information to higher-order centers has not been fully explored (Schoenfeld and Macrides, 1984 and Schoenfeld et al., 1985). It is also possible that

Torin 1 in vitro different subsets of MCs engage different networks in the piriform cortex. Indeed, in a previous publication we showed that a small percent (∼2%) of the odor-divergent MCs did not change the z-score throughout a discrimination session or when odors changed between the rewarded and unrewarded state (Doucette and Restrepo, 2008). Thus, it is possible that a subset of MCs does carry information on odor identity, and the odor responsiveness of MCs within this subset may be minimally affected by behavioral context. Finally, our findings do not exclude the possibility that the same MCs that carry information on odor value also carry information on odor identity through another coding mechanism in either a simultaneous or sequential fashion, as found in taste

cortical neurons (Miller and Katz, 2010). Indeed, regarding sequential transfer of information, it is known that SMCs respond differentially to odors within the first sniff after odor exposure (Cury and Uchida, 2010). These issues deserve future studies. In summary, we find that SMCs separated by large Epothilone B (EPO906, Patupilone) distances (of up to 1.5 mm) and therefore innervating different glomeruli fire synchronously, and that synchronized firing conveys information on odor value, not odor identity. This is particularly relevant because the output from MCs innervating different glomeruli converges on OC pyramidal cells (Apicella et al., 2010), and synchronized firing of MCs is effective at eliciting excitation of OC pyramidal cells (Franks and Isaacson, 2006 and Luna and Schoppa, 2008). Thus, our findings suggest that the circuit encompassing the MCs and the OC pyramidal cells is involved in evaluating information on odor value. Eight 8- to 10-week-old animals were implanted bilaterally with 2 × 4 electrode arrays (Figure 1A). Animals were anesthetized with an intraperitoneal ketamine-xylazine injection (composed of 100 μg/g and 20 μg/g, respectively). The electrode arrays were manufactured by Micro Probes Inc., composed of platinum iridium wire etched to a 2 μm tip, and coated with parylene C (3–4 MΩ at 1 kHz).

These interactions required its S5/P loop/S6 segment (Figure 7B, compare constructs 2 and 3). Replacing this segment with an analogous region of a P/Q/N-type VGCC UNC-2,

or a L-type VGCC EGL-19 also abolished the interaction (Figures S7B and S7C). Other NALCN channel GDC-0449 price components (mUNC-79 and mUNC-80), and an innexin channel (UNC-7), did not exhibit interactions with NLFs (not shown). Our molecular genetic, biochemical and physiological analyses uncover NLF-1/mNLF-1, a conserved ER regulator of a Na+ leak channel NCA/NALCN, which maintains the RMP and activity of a small premotor interneuron network responsible for the maintenance of C. elegans’ rhythmic locomotion ( Figure 7D). Our current data suggest a remarkable functional specificity of NLF-1 with a Na+ leak channel NCA. nlf-1 mutants exhibit behavioral phenotypes unique and characteristic of the loss-of-function mutants for the NCA channel components, with no additional phenotypes from nca(lf). nlf-1 null alleles do not enhance nca(lf) defects in locomotion or in AVA membrane properties. Other C. elegans

cation channel mutants, while uncoordinated in locomotion, do not faint. nlf-1 suppresses the nca(gf) movement pattern but does not suppress that of VGCC(gf) mutants. Genetically, these results place nlf-1 fairly specifically in the biological Gemcitabine nmr pathway as the nca genes. Consistently, all NCA channel component reporters, despite being overexpressed, exhibit drastic reduction of axonal localization in the absence of NLF-1. On the other hand,

sequence-related VGCC reporters are unaffected in nlf-1 mutants, although a subtle difference of endogenous level could be masked by reporter overexpression. NLF-1 may achieve its functional unless specificity as an auxiliary subunit unique for the Na+ leak channel. Multiple lines of evidence, however, suggest NLF-1’s role at the ER. NLF-1, as well as ectopically expressed mNLF-1, are restricted at the ER of C. elegans neurons. mNLF-1 also localizes to the ER in yeast and mammalian cells. Importantly, disrupting NLF-1’s ER localization diminishes, or severely reduces its rescuing ability of nlf-1 mutants. Although many ER proteins are promiscuous facilitators for the folding and delivery of membrane proteins, ER resident proteins with remarkable substrate and functional specificity, such as RIC-3 that facilitates the surface expression of subtype nicotinic acetylcholine receptors (Halevi et al., 2002; Lansdell et al., 2005), CALF-1 that affects axon localization of the C. elegans P/Q/N-type VGCC UNC-2 ( Saheki and Bargmann, 2009), and SARAF that interacts with STIM1 to regulate store-operated calcium entry ( Palty et al., 2012), are present. NLF-1 may represent another example of an emerging class of ER proteins with substrate specificity.

Such off-responses may be a shared feature of nonauditory mechanoreceptors since they have been observed in three other mechanoreceptor neurons in C. elegans ( Kang et al., 2010, Li et al., 2011 and O’Hagan et al., 2005) as well as in cultured dorsal root ganglion neurons ( Poole et al., 2011). As reported for other C. elegans mechanoreceptors ( Kang et al., 2010 and O’Hagan et al., 2005), MRCs decay during force application suggesting that find more either the channels carrying this current or the protein machinery that transfers force to them adapts to sustained force over time. In addition to this rapidly activating current, we found evidence of additional currents that activated

following a delay of tens of milliseconds in some recordings (see Figure S1 available online). The origin of such currents is unknown and we were unable to study them since their size declined Selleckchem GSK1349572 with repeated stimulation.

In this study, we focused on responses to mechanical stimulation that contained only the initial, rapidly activating MRC. We quantified activation and decay rates by fitting MRCs with a modified alpha function (Figure 1B, thick aqua line), as described (O’Hagan et al., 2005). On average, the time constant for MRC activation in wild-type ASH neurons was ∼2 ms while the time constant for decay was 10-fold longer or ∼30 ms (Table 1). Both the activation and decay rates (τ1τ1 and τ2τ2, respectively) are indistinguishable from those reported previously for MRCs in

PLM neurons (O’Hagan et al., 2005), while activation rates are slower than those found in CEP neurons (Kang et al., 2010). (The decay rate for MRCs in CEP has not been reported.) We found that larger forces were required to activate MRCs in ASH than in the gentle touch receptor neuron PLM (O’Hagan et al., 2005). The amplitude of MRCs increased with stimulus strength (Figure 1D) and plotting their amplitude versus force across multiple recordings shows that the half-activation force is ∼11 μN in ASH (Figure 1E). This Dextrose is two orders of magnitude larger than the force required for half-maximal responses in PLM. These data provide further evidence that ASH is functioning as a nociceptor in C. elegans. The latency between stimulus delivery and channel activation was measured as described (O’Hagan et al., 2005) and had an average value of 3.4 ms (Table 1). This time encompasses several events, including the time needed to move the probe in contact with the animal, transmit force from the cuticle to MeT channels and the time needed to activate them. While it is not possible to directly measure all of these time intervals, we can estimate the time required to move the probe from its starting position into contact with the nose from the probe’s intrinsic resonant frequency and the quality of such resonance.

When a significant main effect was detected with ANOVA tests, Bonferroni’s post hoc correction was used to determine significance

between pairwise comparisons. Normalized values are plotted as a percentage of the average value during the baseline period. Unless stated otherwise, reported values are mean ± SEM. For all statistical comparisons, asterisks indicate a significant effect at the following levels of significance: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To assess INK1197 the distribution of all pyramidal neurons in multidimensional space, we performed a K-means cluster analysis in MATLAB (MathWorks). First, we performed Student’s t tests on each electrophysiological property and morphological parameter to compare bursting and regular-spiking neurons. Using only those parameters that were significantly different, we constructed a 15-dimension matrix for all 110 neurons (consisting of seven morphological properties: total basal dendritic length, total tuft dendritic length, average basal branching

order, average tuft branching order, distance to main apical bifurcation, and the number of branch points in the basal PLX4032 research buy and tuft regions; as well as eight electrophysiological properties: input resistance, sag ratio, subthreshold dV/dt, ADP amplitude, threshold of the second spike, maximal dV/dt during the rising and falling phases of the second spike, and the FWHM of the first spike). Initial spike frequency tuclazepam was not included in the cluster analysis, though these values were significantly different between firing types. Based on these values, the K-means test selected k random cells to seed k clusters (n = 2–10). For all 15 normalized parameters, the Euclidian distance from these k seeds was calculated for all remaining cells, and each cell was then assigned to the cluster it was closest to. The cluster centers were then recalculated, and the process was repeated iteratively until the distributions ceased to change. To determine whether the computed clusters represent a single population or arise from multiple cell types, we computed a cluster index from the 15-dimensional matrix, defined as the ratio

of the sum of the square distances from each multidimensional point to its cluster center and the sum of the square distances from each point to the overall mean. This index varies from zero to one, with values close to zero corresponding to very tight clusters. Assuming that the cells were defined by a single multivariate Gaussian (the null hypothesis, which we would expect if these neurons belonged to the same cell type), we calculated a million cluster index values by repeatedly drawing 110 random samples from that distribution. The p value represents the likelihood that the simulated data have a cluster index greater than the experimental data. To determine whether k clusters (2–10) were represented in the data, we applied the jump method of Sugar and James (2003).

, 1983, Bolz et al., 1984, Horton and Sherk, 1984 and Stockton and Slaughter, 1989). If the actions of APB were selective to the mechanisms that establish the receptive field

center of bipolar cells and RGCs without affecting the receptive field surround, then our finding of an emergent Off response in the Pifithrin-�� LGN could simply reflect a selective loss of the receptive field center. Our results and those of past studies, however, do not support such a possibility. In particular, visual response latency is known to be longer for the receptive field surround compared the center (Enroth-Cugell et al., 1983, Dawis et al., 1984, Cai et al., 1997, Usrey et al., 1999 and Allen and Freeman, 2006). Consequently, if emergent Off responses were simply the result of silencing the On-center

response, then the time course of the emergent Off response should be longer than the initial center response, not Decitabine the same or shorter, as reported here. Moreover, and consistent with previous reports, none of the On-center RGCs in this study showed Off responses following APB application (Slaughter and Miller, 1981, Massey et al., 1983 and Stockton and Slaughter, 1989). Both the time course for emergent Off responses and the timing of those responses suggest APB leads to a rapid change in the synaptic strength of functionally silent, mismatched input from Off-center RGCs onto On-center LGN neurons. Specifically, the emergence of Off responses following APB application is too quick for an anatomical reorganization of inputs. Emergent Off responses are more likely the result of changes in the synaptic strength of mismatched retinal inputs or changes in the contributions made by polysynaptic sources. Because emergent Off responses show no evidence of an increase in visual response latency, it seems unlikely that polysynaptic

circuits play a major role, as these circuits should increase response latency. Moreover, extrinsic sources P-type ATPase of polysynaptic input lack the center/surround organization seen for emergent receptive fields. Finally, current understanding of the push/pull organization of LGN receptive fields holds that local GABAergic input onto On-center LGN neurons comes from Off-center cells that provide a “pull” to reinforce, not reverse, the On response (Hirsch, 2003 and Wang et al., 2011). Although the idea of mismatched projections from RGCs to LGN neurons contradicts current models of retinogeniculate circuitry, there is evidence for the existence of these connections in the literature. In particular, studies using cross-correlation analysis to examine the response properties of synaptically-connected RGCs and LGN neurons describe a small percentage of weakly connected cell pairs mismatched in their On/Off or X/Y signature (Mastronarde, 1992 and Usrey et al.