In contrast, we found that levels of phosphorylated

MARCKS, an actin-binding membrane-associated protein ( Hartwig et al., 1992, Li et al., 2008 and Swierczynski and Blackshear, 1995), were significantly higher in Pcdh-γ mutant cortex samples as dendrite branching defects emerged ( Figure 3A). MARCKS phosphorylation was also elevated in Pcdh-γdel/del neuronal cultures ( Figure S3B). This is consistent with the observed dendritic phenotype, because phosphorylation of MARCKS leads to its dissociation from actin and the plasma membrane and results in reduced dendrite complexity in cultured hippocampal neurons ( Hartwig et al., 1992, Li et al., 2008 and Swierczynski and Blackshear, 1995). MARCKS is a classic substrate for PKC,

which phosphorylates it on serine residues 152, 156, and 163 (Heemskerk et al., 1993). PKC activity itself can be a negative regulator JQ1 research buy of dendrite complexity (Metzger and Kapfhammer, 2000), suggesting a possible upregulation of PKC activity in Pcdh-γ mutant cortex. Direct learn more biochemical measurement of PKC activity in cortical membrane preparations showed that it was, indeed, significantly higher between P20 and P24 in mutants compared to controls ( Figures 3B and S3D). We also immunoprecipitated specific PKC isoforms and measured activity from the isolated material. Activities of PKC-α, PKC-δ, and PKC-γ ( Figures S3E–S3H) were all similarly increased in the mutant cortex, suggesting a common mechanism leading to their dysregulation. Classical PKC isoforms, such as PKC-α and PKC-γ, require both intracellular Ca2+ and diacylglycerol (DAG) to become activated, whereas novel isoforms, such as PKC-δ, require only DAG ( Rosse et al., 2010). The fact that all three of these isoforms are hyperactive in Pcdh-γ mutant cortex thus suggested that PLC activation, which leads to production of DAG, might also be elevated. A major brain

isoform, Megestrol Acetate PLCγ1, is activated by phosphorylation at tyrosine 783; in western blots of cortical lysates, Y783-phospho-PLCγ1 levels were indeed significantly higher in mutants at P20 ( Figure 3C). Although aberrant upregulation of PLC and PKC leading to MARCKS hyperphosphorylation is a plausible mechanism for explaining the dendritic defects observed, it leaves open the question of how the γ-Pcdhs regulate such a pathway. Little is known about intracellular binding partners of the γ-Pcdhs; recently, however, it was shown that FAK binds to the γ-Pcdh constant domain, and this inhibits its autophosphorylation on tyrosine residue 397, a key step in its activation (Chen et al., 2009). Additionally, FAK’s Y397 autophosphorylation site interacts with the C-terminal SH2 domain of PLCγ1, and overexpression of FAK can increase PLCγ1 activity indirectly (Zhang et al., 1999 and Tvorogov et al., 2005). We thus examined whether FAK phosphorylation might be aberrantly high in the cortex of postnatal Pcdh-γ mutants.

When the MC was in the low firing rate regime, a clear increase in firing could be observed during light stimulation, followed by a decrease (Figure 6C). When the same MC was firing at a higher rate, excitation was less prominent (Figure 6D). We analyzed the significance of the excitatory effect by comparing our data to 100,000 randomly aligned

histograms (see Experimental Procedures for details). We found three of six cells to have a significant excitatory response (p < 0.01). Population analysis of these experiments, with the firing rate of each cell normalized to the prestimulus period, is shown in Figures 6E and 6F. With 240 pA current injection, AON input had a dual effect consisting selleck inhibitor of a brief increase in firing probability followed by a more prolonged decrease. On average firing probability was increased

to a peak of 9.5 ± 11.3 times the baseline with a latency of 7 ± 1.7 ms (n = 6; Figure 6E). The average firing in the 10 ms periods of light stimulation was 5 ± 7.8 times the rate during the 10 ms right before stimulation (n = 6, p < 0.01, rank-sum test). In the 15 ms following light stimulation, firing was reduced to 0.4 ± 0.5 of baseline values (p < 0.05, rank-sum test) (Figure 6E). With 300 pA, EPZ6438 AON input had a smaller effect on firing probability during light stimulation, increasing it to a peak of 2.0 ± 0.5 times the baseline, and an average increase of 1.8 ± 0.7 times baseline values in the 10 ms period of light stimulation (n = 6; p < 0.01, rank-sum test). The inhibitory effect with 300 pA was manifested as a decrease of the average firing rate to 0.5 ± 0.5 of baseline click here values (p < 0.05, rank-sum test; Figure 6F).

This inhibition was followed by a rebound increase in firing rate presumably due to the intrinsic biophysical properties of MCs (Balu and Strowbridge, 2007). These results indicate that AON inputs can have multiple effects on MCs, depending on their ongoing activity, in part due to the newly discovered direct excitatory inputs. We next tested the functional significance of the AON inputs to MCs in vivo. We used tungsten electrodes to record the activity of single MCs from the dorsal OB in anesthetized rats 2–4 weeks postinjection of the virus. Breathing was continuously monitored with a piezoelectric belt that was wrapped around the rat’s torso and a light stimulus consisting of a pair of 40 ms stimuli, separated by 50 ms, was delivered every 15 s. Putative MCs/TCs were identified based on their depth and their strong breathing related firing pattern (Macrides and Chorover, 1972). Previous studies have noted that GCs are not visible to extracellular electrodes (Kay and Laurent, 1999; Rinberg et al., 2006; Doucette et al., 2011). Figure 7A shows an example of such an experiment. Single units were identified by stereotyped spike waveforms identified using cluster analysis (Figure 7A1). Figure 7A2 shows five traces aligned by the light stimulus (blue square).

, 2007), suggesting that most cells recognize the decreased stability of the GSK126 protein and can effectively target the polypeptide for degradation. However, motor neurons may not effectively induce a stress response to protein misfolding (Batulan et al., 2003), leaving them vulnerable to the dominant-negative effect of the G59S mutation. In contrast, the distinct morphology of dopaminergic neurons may make these cells uniquely vulnerable to defects in the initiation of retrograde transport. The immense axonal arborizations of dopaminergic neurons (Matsuda et al., 2009) suggest that the loss-of-function effects of the Perry mutations may critically

affect this cell type. Thus, our data inform normal dynein-dynactin function as well as the selective vulnerabilities of discrete populations of neurons to specific perturbations in the cellular function of these proteins. The two distinct mechanisms we propose for the pathogenesis of HMN7B and Perry syndrome highlight the specialized function of a single domain of dynactin and provide a model for the function of the CAP-Gly domain of p150Glued in neurons. Dorsal root ganglia (DRGs) were dissected from adult mice less than 1 year old and treated with 20 U/ml papain, followed by

2 mg/ml collagenase II and 2.4 mg/ml dispase II. Neurons were then dissociated in HBSS, supplemented with 5 mM HEPES and 10 mM D-glucose (pH 7.35), and purified through a 20% Percoll gradient for 8 min at 1,000 × g. DRG neurons were transfected with either HDAC inhibitor DNA, siRNAs B3GAT3 or both using the basic neuron SCN nucleofector kit (Lonza) and then plated onto 0.01% poly-L-lysine and 20 μg/ml laminin coated coverslips or glass-bottom dishes (FluoroDish, World Precision Instruments) and grown for 2–4 days in F-12 medium supplemented with 10% heat-inactivated FBS and 100 U/ml penicillin-streptomycin.

Live-cell imaging was done in Hibernate-A (Brain Bits, Springfield IL) supplemented with 2% B27 supplement (Invitrogen) and 2 mM GlutaMax (GIBCO). All experiments involving animals were approved by the IACUC at the University of Pennsylvania. Images of LAMP1-RFP motility (Figures 1 and 7) were acquired 366 ms per frame for 360 frames. Images were acquired in epifluorescence on an inverted Leica DMI-6000/CTR-7000HS microscope with an Apochromat 63× 1.4 NA oil-immersion objective in a temperature-controlled chamber (37°C) with an ORCA R2 (Hamamatsu) camera using LAS-AF (Leica) software. Fixed cells were imaged in epifluorescence, as described above. Neurons were imaged with a 1.6× magnifier. Photobleaching of LAMP1-RFP (Figures 5A, 8C, and S4) and FRAP of EGFP or EGFP-p150Glued (Figure 4A) was performed in a temperature-controlled chamber (37°C) using the 561 nm or 488 nm laser, respectively, at 100% power for 25 cycles on the Ultraview Vox (PerkinElmer) spinning disk confocal system with an Ultraview Photokinesis (PerkinElmer) unit on an inverted Nikon Ti microscope with apochromat 100× 1.

, 1999 and Sturgill et al., 2009). While PSD-95 contains six cysteines, only two of these (C3 and C5) occur in the N-terminal fragment (amino acids 1-433), which we designate PSD-95-1-433. We observe similar levels of nitrosylation for full-length PSD-95 and PSD-95-1-433 in HEK-nNOS cells (Figure 1D). In HEK-nNOS cells nitrosylation of PSD-95-1-433 is abolished with mutation of both C3 and C5 with intermediate effects in

the individual C3 and C5 mutants (Figure 1E). Because PSD-95 is palmitoylated and nitrosylated at the same cysteines, we wondered whether the two processes might be mutually competitive. Consistent with this hypothesis, stable overexpression of this website nNOS in HEK293 cells substantially diminishes palmitoylation of full-length PSD-95 as measured by [3H]palmitate incorporation (Figure 2A). Inhibition of nNOS in these cells by the nNOS-selective inhibitor N5-(1-imino-3-butenyl)-L-ornithine (L-VNIO) increases PSD-95 palmitoylation, while transient expression of nNOS in 293 cells reduces PSD-95 palmitoylation when measured directly by the acyl biotin exchange (ABE) procedure (Figures 2B and 2C). This method is analogous to the biotin-switch method but uses hydroxylamine to reverse palmitoylation (Drisdel et al., 2006). To confirm that NO is the mediator of these effects, we treated 293 cells with Cys-NO, which markedly reduces [3H]palmitate

incorporation into PSD-95 (Figure 2D). NO donor treatment similarly GDC-0449 concentration reduces palmitoylation of PSD-95-1-433. The action of NO upon PSD-95 palmitoylation is selective. Thus, HRas is physiologically palmitoylated and nitrosylated, but the two processes occur at different sites of the protein (Hancock et al., 1989 and Lander et al., 1996). NO donor treatment fails to alter palmitoylation of HRas (Figure 2E). We explored the influence Calpain of

NO upon palmitoylation of PSD-95 in mammalian brain. In both cerebellar granule and hippocampal cultures, palmitoylation, monitored with [3H]palmitate, is virtually abolished by NO donor treatment (Figures 3A and 3B) concomitant with increases in PSD-95 nitrosylation (Figures 3C and 3D). We wondered whether endogenous NO regulates palmitoylation of PSD-95 in the brain. Because nNOS is highly expressed in granule cells of the cerebellum, we chose to focus on this system. Utilizing the ABE assay in cerebellar granule cells, we detect robust palmitoylation of endogenous PSD-95, which is significantly enhanced by treatment with L-VNIO (Figure 3E). Thus, endogenous NO physiologically diminishes levels of PSD-95 palmitoylation. Blocking synaptic activity with tetrodotoxin (TTX) increases palmitoylation of several proteins including PSD-95 (Hayashi et al., 2009 and Noritake et al., 2009). Nitrosylation of PSD-95 is decreased and palmitoylation increased in neurons treated with TTX (Figures 3F and 3G).

First, we counted the occurrences of each possible electrical triplet pattern (Figure 4A). The recorded quadruplets were separated

into triplets for a total of n = 173 triplets. The intersomatic distances measured for each configuration were used to predict the probability of electrical and chemical connections for the nonuniform random model. The occurrences predicted by both random models were counted in the same way as for the data (Supplemental Experimental Procedures). The ratio (data/prediction) indicates the relative occurrence of each of the four possible nonisomorphic patterns, Paclitaxel compared to the two random connectivity predictions (Figure 4A). We found that the predictions of both random check details connectivity models differ from the data. The uniform random prediction shows large deviations compared to the data for most patterns (p values: p1 = 0.003, p2 = 0.022, p3 = 0.0004, p4 = 0.0004), confirming that the model is insufficient to describe the statistics of connections of the MLI network. The nonuniform random prediction also deviates from the data but to a lesser degree, as the occurrence of fully connected triplets (pattern 4) is correctly predicted (p values: p1 = 0.0004, p2 = 0.213, p3 = 0.0004, p4 = 0.202). We separately confirmed that the fully interconnected triplets

(pattern 4) are indeed the result of direct connections and not indirect electrical coupling (Figure S4E). To characterize the electrical connectivity with a single measure and compare it to random connectivity models, we used the clustering coefficient C. C was originally introduced as a measure of the topological organization of networks and used to Carnitine dehydrogenase highlight differences between small-world networks and random networks, whose average C are significantly different ( Watts and Strogatz, 1998). C is usually measured for each node

in a network. Here, we calculate C for the recorded subnetworks of triplets and quadruplets of MLIs and compute the average over the configurations where C could be measured ( Supplemental Experimental Procedures). It should be noted that the average C obtained in this way is not intended to represent the average C of the whole network but is used to compare with C predicted by random connectivity models, where it was also calculated for subnetworks of triplets and quadruplets. For triplets, C effectively measures the likelihood that if neurons A and B, and B and C are connected, then A and C are also connected. The nonuniform random model predicted a higher clustering coefficient for electrical synapses, CE, than the uniform random model. This is expected if the tested neurons are sampled locally, as they were in the experiments ( Figures S2B and S2C). However, CE of the data significantly exceeds even the nonuniform random prediction ( Figure 4B; uniform random p = 0.0001; nonuniform random p = 0.0001).

At the time of choice, the AI might signal cue negative value (i.e., punishment prediction),

which could drive avoidance behavior. This is in line with theories proposing that brain areas involved in somatic affective representations are causally responsible for making a choice (Jones et al., 2010; Naqvi and Bechara, 2009; Craig, 2003). The flattened punishment-learning curves following DS preferential atrophy in presymptomatic HD patients was specifically captured by a higher choice randomness. Contrary to reinforcement magnitude and learning rate, this parameter impacts the choice, not the learning process. This is consistent with our fMRI finding that the DS was active at punishment cue display (during choice period), but not at outcome display (during learning period). It accords well with the idea that the DS is the “actor” GW3965 solubility dmso part of the striatum, the “critic” part being more ventral (O’Doherty et al., 2004; Atallah et al., 2007). Indeed, the transition from presymptomatic to symptomatic HD, which was characterized by degeneration extending to the VS, was captured by a lower reinforcement magnitude in the gain condition. Thus the VS, which is closely linked to the VMPFC, would play a role similar to that of the insula, but for learning positive instead of negative values. This is in line with studies implicating the VS and VMPFC in encoding both reward predictions

at cue display and reward prediction errors Chlormezanone at outcome display (Rutledge mTOR inhibitor et al., 2010; Palminteri et al., 2009a; Hare et al., 2008). However, interpreting the specific role of the DS in choosing between aversive cues remains speculative. The link with choice randomness might suggest that the DS is involved in comparing

negative value estimates or in integrating the precision of these estimates, or in adjusting the balance between exploration and exploitation. Another possibility is that the DS is specifically involved in avoidance behavior, i.e., in inhibiting the selection of the worst option and facilitating the selection of alternatives. This interpretation is endorsed by the observation that input connections to the caudate head come from dorsal prefrontal structures, which have been implicated in inhibitory and executive processes (Draganski et al., 2008; Haber, 2003; Postuma and Dagher, 2006). In conclusion, we found evidence that the AI and DS are causally implicated in punishment-based avoidance learning, but for different reasons. The AI might participate by signaling punishment magnitude, in accordance with its involvement in negative affective reactions, whereas the DS might participate by implementing avoidance choices, in accordance with its involvement in executive processes. These findings suggest the existence of a distinct punishment system underpinning avoidance learning, just as the reward system underpins approach learning.

, 1999). Considering the association between cannabis use and psychiatric disorders (e.g. Degenhardt et al., 2012, Lev-Ran et al., 2013 and Zammit et al., 2002), there are

reasons to believe that cannabis use would be associated with DP. In this study, we will therefor make use of a cohort study spanning over nearly 40 years to investigate (1) if there is an association between cannabis use in adolescence and future DP and (2) if possible associations persist after adjustment for a number of potential covariates. The study cohort, comprising 49,321 Swedish men has been described in detail elsewhere (Andréasson et al., 1987). In short, our study is a register follow-up to the cohort study including all Swedish men born in 1949–1951 Talazoparib datasheet who were conscripted to compulsory military service in 1969–1970 (aged 18–20 years). The cohort covers approximately 97.7% of the Swedish male population at that time. Those not participating were exempted due to severe handicaps or congenital disorders. At time for conscription all men answered two questionnaires, one focused on alcohol consumption, tobacco

and illicit drug use, and the other was based on questions on family and social see more background, school performance, psychological factors, behavior and adjustment and self-rated health. In addition to this, they went through physical and psychological tests and a physician diagnosed physical and mental disorders according to the Swedish

version of the International Classification of Disease (ICD) 8th revision (ICD-8). Those with a psychiatric disorder were also examined by a psychiatrist. The study exposure is self-reported cannabis use at time for conscription. Questions were asked whether subjects had ever used drugs (including cannabis), which drugs had ever been used, first drug used, drug most commonly used, frequency of use and questions regarding use of specific drugs from a list with alternatives. The question about frequency of use had Masitinib (AB1010) fixed response alternatives; never, 1–2 times, 3–10 times (those two categories were collapsed into one; 1–10 times), 11–50 times and >50 times, that were used in our analyses. The study outcome is first time of being granted DP between 20 and 59 years of age. Data on DP was collected from the National Social Insurance Agency for the years 1971 to 1989 and from Longitudinal Register of Education and Labor Market Statistics from 1990 to 2008. DP was categorized into three groups, i.e., overall (aged 20–59), early DP (aged 20–39) and late DP (aged 40–59). A majority of all disability pensions occur during the second part of working life, i.e. after the age of 40. Based on previous studies on DP, we accounted for the following covariates: Social background including childhood socioeconomic position (SEP), i.e.

Indeed, model-based learners do not rely on model-based RPEs: the learning problem they face—tracking state transition probabilities and immediate rewards rather than cumulative future rewards—demands different training signals (Gläscher et al., 2010). This apparent mismatch encourages consideration see more of a hybrid of a different sort. We have so far examined theories in which model-based and model-free predictions compete directly to select actions (Daw et al., 2005). However, model-based and model-free RPEs could also usefully be integrated for training. For instance, consider the standard

actor-critic account (Barto et al., 1983 and Barto, 1995). This uses RPEs derived from model-free predictions (the critic) to reinforce action selection policies (the actor). Errors in model-based predictions, if available, could serve the same purpose. A model-free actor trained, in part, by such a model-based critic would, in effect, cache (Daw et al.,

2005) or memorize the recommendations of a model-based planner, and could execute them subsequently without additional planning. The computational literature on RL includes some related ideas in algorithms, such as prioritized Everolimus mouse sweeping (Moore and Atkeson, 1993), which caches the results of model-based evaluation (albeit without a model-free component), and Dyna (Johnson and Redish, 2005 and Sutton, 1990), which trains a model-free algorithm (though offline) using simulated experiences generated from a world model. In neuroscience, PD184352 (CI-1040) various theories have been proposed in which a world model impacts the input to the model-free system (Bertin et al., 2007, Daw et al., 2006a, Doya, 1999 and Doya et al., 2002). The architecture suggested here more closely resembles the “biased” learning hypothesized by Doll et al. (2009), according to which top-down information (there provided by experimenter instructions rather than a learned world model) modifies the target of model-free RL. Outside the domain of learning, striatal BOLD responses are indeed affected by values communicated by instruction rather than experience (Fitzgerald

et al., 2010 and Tom et al., 2007) and also by emotional self-regulation (Delgado et al., 2008). Further theoretical work is needed to characterize the different algorithms suggested by this general architecture. However, in general, by preserving the overall structure of parallel model-based and model-free systems—albeit systems that would exchange information at an earlier level—the proposal of a model-based critic would appear to remain consistent with the lesion data suggesting that the systems can function in isolation (Killcross and Coutureau, 2003, Yin et al., 2004 and Yin et al., 2005), and with behavioral data demonstrating that distinct decision systems may have different properties and can be differentially engaged in different circumstances (Doeller and Burgess, 2008, Frank et al., 2007 and Fu and Anderson, 2008).

, 2010 and Leutgeb et al., 2007; Figure 1D). Along these lines, while much of the behavioral literature arguing for a pattern separation function is consistent, there are also alternative explanations. Instead of studying the ability of animals to distinguish Selleckchem UMI-77 different input patterns concurrently,

the behavioral studies of the roles of the DG and neurogenesis in pattern separation have typically been designed to examine how animals’ responses to their present situation can be altered by their memories of the past input patterns (which are different from the current ones). Two types of strategies have been used in behavioral tasks for pattern separation. In some tasks, animals were trained to NLG919 molecular weight distinguish two input patterns, such as conditioned (CS+) and unconditioned (CS−) contexts. Specifically, initial training enabled the animals to generalize their conditioned responses to both CS+ and CS− contexts, and their ability to discriminate the CS+ and CS− contexts was subsequently tested through continuing reinforcement of the CS+ context but not the CS− context (McHugh et al., 2007 and Sahay et al., 2011).

It is possible that performance changes resulting from alterations in DG and/or neurogenesis may be due to defects in pattern separation, but it is also possible that other processes, such as inhibitory learning, may be involved. In other tasks, animals were trained Exoribonuclease to learn one pattern and were subsequently tested, using a working memory framework, for their ability to discriminate a learned pattern from another pattern (Clelland et al., 2009, Creer et al., 2010, Gilbert et al., 2001, Hunsaker and Kesner, 2008 and Saxe et al., 2007). Paradigms using this type of task are also able to evaluate behavioral performance as a function

of the extent of input pattern differences such as by varying the distance of spatial location systematically in the cheeseboard spatial discrimination task (Gilbert et al., 2001), further supporting a relationship between the pattern separation ability and behavioral outcome. However, it remains difficult to rule out in these tasks that animals may solve the task using different neural pathways according to the degree of dissimilarity between the input patterns. For example, in the cheeseboard spatial discrimination task, lesions of CA1 did not affect the performance at any of five tested pattern separation degrees, suggesting that the task could be solved independent of the trisynaptic pathway (Gilbert et al., 2001). On the other hand, lesions of CA3 affect working memory in general, making it difficult to test whether pattern separation relies on CA3 outputs other than Schaffer collaterals (Gilbert and Kesner, 2006). Finally, there is a lack of a clear role for young neurons that would make them advantageous in the classic mechanism by which the DG provides separation.

The hyperpolarization-induced suppression of subsequent buy Trichostatin A firing was blocked by internal TEA and by external α-dendrotoxin (Figure 6 and Figure 7). Furthermore, somatic membrane patches showed characteristic properties of a KDR current (activation at ∼−25 mV) and steady-state inactivation at Vrest could be removed by hyperpolarization (Figure 8). The collected results support a KDR mechanism whereby hyperpolarizations from Vrest increase the available KDR channel pool and suppress firing during subsequent depolarization. The study of intrinsic mechanisms for adaptation in intact cells and circuits is challenging and requires complementary experimental approaches. Some of these approaches

yielded unexpected results worth mentioning. For example, external α-dendrotoxin increased firing significantly, whereas internal TEA (20 mM) did not (Figure 6 and Figure 7), even though TEA is a more general blocker of K channels. However,

TEA increased the spike width substantially. The increased spike width increases the time that unblocked K channels could be activated and also possibly leads to increased Na channel inactivation. Thus, unintended effects on K and Na channels may have counteracted any increase in firing rate caused by specifically blocking α-dendrotoxin-sensitive KDR channels. Another unexpected result was that hyperpolarizing current had no effect on subsequent firing to weak visual stimulation but enhanced slightly the firing to weak current injection (Figure 3). The distinct effects may be explained click here by the different time courses of the stimuli: the current injection was a square-pulse, whereas the low-contrast synaptic input was necessarily more sluggish due to the filtering by retinal circuitry. However, in general, similar results were

elicited by protocols that used either current injection or synaptic stimulation as the test stimulus (Figure 1, Figure 2 and Figure 4). Somatic membrane patch recordings showed several properties of KDR currents that could explain their role in contrast adaptation. First, the channels activate at voltages traversed during an action potential (activation at −25 mV). Second, channel inactivation at Vrest could be removed by hyperpolarization (Figure 8). Thus, a period of Mirabegron hyperpolarization would increase the number of available channels, which could then be activated during a subsequent burst of firing. The initial spikes in the burst would be largely unaffected, but channels opened during these initial spikes would suppress subsequent spikes (Figure 2) because KDR deactivation is relatively slow (Figure 8D) compared to the typical interspike interval during the initial spike burst (Figure 1); Kv1 channels also contribute to the interspike interval in neocortical pyramidal cells (Guan et al., 2007).