We note, however, that this refinement may not only depend on visual experience, as dark-rearing may also delay the development of visual circuits (Fagiolini et al., 1994, Iwai et al., 2003 and Espinosa and Stryker, 2012). Cortical inhibition likely plays a role in surround-induced response suppression in V1 (Haider et al., 2010, Kinase Inhibitor Library Adesnik et al., 2012 and Nienborg

et al., 2013). Our results extend this idea by revealing how costimulation of the RF surround affects membrane potential dynamics to suppress neuronal firing; while the average membrane potential was altered little by surround stimulation, the principal effect of the surround was to counteract membrane depolarization generated by stimulation of the RF alone. Specifically, we observed an experience-dependent increase of relative membrane hyperpolarization by natural surround stimuli at times of large depolarizing events during RF stimulation. This hyperpolarization was partly mediated by an increased Cl− conductance, Selleckchem DAPT most likely through GABAA receptors. Yet the average firing rates of PV and SOM interneurons, although slightly reduced by surround stimulation, were not different between natural compared

to phase-randomized surround stimulation in mature V1. Hence, the preferential sensitivity for natural scene statistics in the surround was not mediated by a relative increase of inhibitory tone. Rather, we identified transient increases in membrane hyperpolarization during natural relative to phase-randomized surround stimulation, particularly at times that coincided with moments of greatest depolarization during RF stimulation. These temporal differences in the magnitude of hyperpolarization resulted in increased spike suppression,

and thereby increased the response selectivity for features in full-field natural scenes in mature V1, but not in the immature or visually deprived V1. Therefore, our results suggest that sensory experience during maturation exerts a prominent influence on the recruitment of inhibition—particularly with respect to its timing relative to potential firing events—to generate more already selective coding of visual features embedded in natural scenes. Our results are broadly consistent with observations in cat V1, where there is a transient increase of inhibition during surround suppression with drifting grating stimuli (Ozeki et al., 2009), which ultimately results in an overall reduction of both excitatory and inhibitory conductances when the circuit reaches a balanced state. Our results, however, underscore the importance of transient hyperpolarization prior to spiking events as a mechanism for effective surround suppression during ongoing stimulation with natural movies. A probable explanation for this difference is that the statistical properties of grating stimuli are much narrower than that of the naturalistic stimuli used in our study.

Specifically, a deletion of a synaptic isoform of the LAR-type receptor phosphotyrosine phosphatase PTP-3 was found to cause mislocalization of α-liprin, whereas a deletion of α-liprin caused mislocalization of the synaptic isoform of PTP-3 ( Ackley et al., 2005). Moreover, a gain-of-function point mutation in the LH1 domain of α-liprin suppressed the phenotype caused by a loss of SYD-1 (a rho GAP that is essential for synapse assembly in invertebrates, but whose vertebrate homolog

has not yet been identified; Dai et al., 2006 and Owald et al., 2010). Strikingly, the α-liprin gain-of-function mutation increased α-liprin binding to ELKS. In addition, the ability of the α-liprin gain-of-function mutation to rescue the syd-1 PCI 32765 mutation required ELKS ( Dai et al., 2006), although ELKS mutations otherwise did not appear to cause any phenotype in C. elegans ( Deken et al., 2005). Furthermore, the homodimerization of α-liprin appears to be essential for its ability to suppress the loss-of-function effect of syd-1 mutations ( Taru and Jin, 2011), suggesting overall that the syd-1 loss-of-function is rescued by an α-liprin homodimer that exhibits increased binding to ELKS. Together, these data established that active zone formation with recruitment of synaptic vesicles and of LAR-type receptor phosphotyrosine phosphatases requires α-liprin,

possibly by simultaneous binding of α-liprin to the receptor phosphotyrosine phosphatase, RIM, ELKS, Syd-1, and itself. The data thus suggest a model whereby α-liprin acts to link synaptic cell adhesion to the RIM/Munc13/RIM-BP Alectinib core complex

that recruits vesicles and Ca2+ channels to active zones. However, the current understanding of α-liprins is incomplete. Many pressing questions remain, from simple questions about the possible role of β-liprins (see Wang and Wang, 2009 and Astigarraga et al., 2010), to complex issues such as how α-liprins exactly organize a nerve terminal. Why does the active zone become apparently bigger in α-liprin mutants? What is the role Oxygenase of the LAR tyrosine phosphatase activity in synapse assembly and function, if any? How do α-liprin mutations affect neurotransmitter release, which is—after all—what the nerve terminal does? And finally, is the α-liprin function uncovered in C. elegans paradigmatic of its function elsewhere? Moreover, a more fundamental biophysical description of the protein complexes involving α-liprins is needed, as illustrated by the puzzling observation that the gain-of-function α-liprin mutation in C. elegans that increases ELKS binding ( Dai et al., 2006) is in a region of the protein that in studies of mammalian proteins was not involved in ELKS binding ( Ko et al., 2003a). Of the five core active zone proteins, ELKS is the most enigmatic. ELKS was discovered when a translocation in papillary thyroid carcinoma was found to place the ELKS gene upstream of the RET tyrosine kinase, thereby activating it (Nakata et al., 1999).

There was also a significant interaction condition × band (F2,38 = 38.50; p < 0.001, pη2 = 0.67), reflecting a stronger variability during movie in the low (0.005–0.10 Hz) (p < 0.001) and middle (0.1–0.2 Hz) (p = 0.002) frequency bands (Bonferroni post-hoc test) (Figure 8B). Fluctuations of β BLP correlation did not reveal any significant modulation (p > 0.05). Importantly, the same analysis computed for the cross-network interaction between the visual and language network (θ and β BLP) did not reveal any significant effect (p > 0.05). This suggests that

the enhanced correlation between these two networks was stationary. Then, we considered the putative dependence of nonstationary properties of BLP correlation within the visual network upon specific features of the movie. Based on the observation that inter-regional BLP correlations are stronger at frequencies below 0.1 Hz, and that its variability is stronger even at www.selleckchem.com/products/pci-32765.html lower frequencies (0.005–0.10 Hz) (Figure 8B), it is sensible to assume that events occurring on a similar time scale may represent an ideal candidate to modulate the α BLP correlation. Psychological studies have shown that subjects perceive natural stimuli in temporal chunks that can be defined by event boundaries occurring MK-1775 purchase at multiple timescales, ranging from fine-grained (a couple of seconds or less) to a coarse-grained scale (few tens of seconds) (Zacks et al., 2007 and Zacks and

Swallow, 2007). These “event boundaries” are associated to specific neural responses in visual and attention areas (Sridharan et al., 2007 and Zacks et al., 2001) as seen through fMRI. Hence, we hypothesized

that the nonstationarity of power correlation in visual cortex was partly dependent on the perception first of event boundaries in the movie. To test this hypothesis, we carried out a psychophysical control experiment on an independent sample of 12 participants, who were asked to segment the movie in temporal chunks that they found natural and meaningful (Supplemental Information). Our observers perceived the movie as structured into discrete events, and interestingly, event boundaries occurred at similar times in the majority of subjects (Figure 8C). To examine the existence of possible temporal relationships between the emergence of transient drops of α BLP correlation (Figure 8A) and event boundary time series (Figure 8C), the two time series were binarized (Supplemental Information) and studied with lagged cross-correlation (Figures 8D–8F). Bootstrapping was used to determine a significant correlation threshold (r = 0.125, p = 0.001). In the first movie block, the highest significant correlation (r = 0.33, p < 0.01) between the two binarized time series occurred at lag = 23 s (Figure 8F). A second significant peak of correlation (r = 0.25, p < 0.01) occurred at around 36 s (see marks). In the second movie block a significant correlation peak (r = 0.30, p < 0.01) was identified at lag = 37 s.

One caveat to this idea, however, is that dopamine works through metabotropic ion channels (Gazi and Strange, 2002), and the dynamics of the second messenger cascades activated by dopamine receptors are apparently not fast enough to affect neural activity on rapid time scales (Lavin Ivacaftor concentration et al., 2005). The effects could, however, be driven by glutamate co-released from dopamine neurons (Lavin et al., 2005). A second possibility is that the value signal is carried by the substantial input from the centromedian/parafascicular

(CM-PF) thalamic nuclei (Nakano et al., 1990). A majority of neurons in CM-PF respond when low-value actions are required (Matsumoto et al., 2001 and Minamimoto et al., 2005). An additional possibility is that the increased value representation is coming from other areas of frontal cortex, for example dorsal anterior cingulate projections to the striatum. This area has a strong value representation (Kennerley and Wallis, 2009), and it sends projections into the striatum that slightly overlap with the lPFC projection (Haber et al., 2006). The projections from this area, however, do not appear to project directly to the portion of the dorsal striatum from which we recorded (Haber et al., 2006). Overall, then, the mostly likely candidates for a fast value-related signal in the striatum would be glutamate coreleased from dopamine neurons, or the CM-PF input. Examination of the

neural representation of color bias and sequence in the fixed condition Linsitinib cell line showed that they followed complementary patterns, such that sequence information increased in lPFC and color

bias information decreased in dSTR as the monkeys learned within each block. The increase and decrease were significantly related to the relative behavioral weight of sequence and color information, estimated by a Bayesian behavioral model. Thus, when the sequence switched, the animals reverted to using either the pixel information as they relearned the sequence, and this could be seen in both the behavioral and neural data. As they learned the sequence they transitioned to using less pixel information, which was less accurate, and more sequence information, which was more accurate. This tradeoff is consistent, at a high level, with a model which has suggested that dual control systems, one in lPFC and one in the dSTR, compete for control of behavior (Daw et al., 2005). This model suggests that the tradeoff between these systems is mediated by optimal integration based on the uncertainty associated with the predictions of each system. In other words, if one system is producing uncertain estimates, it is weighted less in the decision process. Thus, our data is consistent with this aspect of the model. What is less clear from our data, however, is whether the dSTR does action selection when action values are high, and the sequences can be executed like habits. A different task structure might make this clearer.

, 2011) and the task structure required differences in firing rates in the two populations during target achievement. We therefore performed a thinning procedure to equate firing rates in the two populations (Gregoriou et al., 2009; Experimental Procedures).

Despite differences in firing rate being removed, there remained a significant difference in spike-field FG-4592 mw coherence between output cells and indirect cells (Figure S3; p < 0.001, Bonferroni corrected), demonstrating that this effect was not driven by firing rate differences. To further ensure that our results were not affected by firing rate, we separated our analysis by cell and trial type to examine trials in which output cells were required to increase their firing rate to achieve the target and trials in which output cells decreased their firing rate (Figure S3). There was still a significant difference in coherence between output cells that decreased their firing rate relative to indirect cells (p < 0.05, Bonferroni corrected), despite no significant difference BLZ945 in firing rate between these populations. Finally, we also calculated

coherence after removing cells with low signal-to-noise ratio (SNR) from the indirect population and coherence remained higher in output cells than indirect cells, demonstrating that the effect was not due to differences in SNR (Figure S3; p < 0.05, Bonferroni corrected). These coherent interactions were greatly diminished between trials when rats were not actively engaged in the task (Figure 3D). Furthermore, during these periods, the difference in coherence between output and indirect populations

was abolished (Figures 3E and 3F). These results show that the corticostriatal coherence that emerged during learning was highly specific for neurons that are directly relevant to behavioral output, even when they are closely intermingled with other cells, and that these precise interactions are flexible and appear rapidly as needed during task performance. Because we about found that M1 spikes occurred preferentially at the peak of the DS LFP (Figure 2B), we next investigated the phase offset of the spike-field coherence. From the mean phase heat map, we see that there is a consistent negative phase offset in the 6–14 Hz range (Figure 4A). By convention, this suggests that M1 spikes precede the peak of the DS LFP in the 6–14 Hz band. Indeed, the phase at 6–14 Hz was commonly negative, as can be seen in the distribution of phase offsets for every cell and every frequency from 6 to 14 Hz (Figure 4B). When phase offset values are used to estimate a temporal delay between M1 spikes and DS LFP (see Experimental Procedures), we see a clear preference for M1 cells to fire at an offset of −5 to −7 ms relative to the DS LFP, as reflected in the mode of this distribution (Figure 4C; SEM = 0.03 ms).

Previous studies had established that interactions of tyrosine-based signals with the μ subunits of AP-2, AP-3, and AP-4 mediate various cargo sorting events, including rapid internalization from the plasma membrane, transport to lysosomes and melanosomes, and direct delivery from the TGN to endosomes (Bonifacino and Traub, 2003; Robinson, 2004;

Burgos DNA Damage inhibitor et al., 2010). The μ1A subunit of AP-1 was also known to interact with YXXØ-type signals (Ohno et al., 1995), but the functional significance of these interactions remained unclear. Our findings now show that YXXØ-μ1A interactions play a critical role in cargo sorting to the neuronal somatodendritic domain. The YNQV sequence from CAR behaves as a typical YXXØ signal, in that both the Y and V residues are required for somatodendritic sorting as well as interaction with μ1A (Figure S3) (Carvajal-Gonzalez

et al., 2012). Furthermore, this sequence binds to a site on μ1A that is similar to the structurally defined YXXØ-binding site on μ2 (Figure 2) (Owen and Evans, 1998). The YTRF sequence from TfR also fits the canonical YXXØ motif, and both the Y and F residues are necessary for somatodendritic sorting (Figure 1) and μ1A binding (Figure S1). However, this sequence seems to bind to a different site on μ1A that only shares W408 with the conserved Epigenetic inhibitor library site ADAMTS5 (Figure 2). This observation points to a potentially

new mode of signal recognition by μ subunits. Our findings highlight both similarities and differences in the mechanisms of somatodendritic sorting in neurons and basolateral sorting in epithelial cells. Among the similarities, interactions of signals with the μ1 subunit of AP-1 underlie both of these polarized sorting events. In addition, the same YXXØ signal in CAR, YNQV, mediates somatodendritic (Figure S3) and basolateral sorting (Cohen et al., 2001; Carvajal-Gonzalez et al., 2012). In the case of TfR, however, basolateral sorting does not depend on the YXXØ signal, YTRF, but on a noncanonical sequence, GDNS (residues 31–34) (Odorizzi and Trowbridge, 1997). We found that mutation of the GDNS sequence has no effect on somatodendritic sorting of TfR (data not shown), in agreement with results from a previous deletion analysis (West et al., 1997). Another key difference is that basolateral sorting of various cargoes, including TfR and CAR, depends mainly on the epithelial-specific μ1B instead of the ubiquitous μ1A (Fölsch et al., 1999; Gravotta et al., 2012; Carvajal-Gonzalez et al., 2012). These variations probably represent adaptations of a basic molecular recognition event to the need for achieving polarized sorting in cell types with very different structural and functional organizations.

, 2010 and Leutgeb et al., 2007). While the rate coding between contexts could be shown to be consistent with a pattern separation function, this observation was clearly at odds with the presumed population coding mechanism of the DG (Treves et al., 2008). One potential explanation suggested by the authors is that these broadly tuned DG neurons were in fact adult-born GCs, with older neurons having “retired” from the network (Alme et al., 2010). The prediction that

the broadly tuned DG neurons observed in vivo belong to an immature population of GCs is consistent with the role for immature neurons in memory resolution above. Nonetheless, the memory resolution hypothesis still predicts GSK-3 inhibitor a population of GCs that are highly specific to a given context. Similarly, supporting evidence selleck chemical can be found in a mouse model where plasticity in the DG was impaired by a conditional

knockout of NMDA (McHugh et al., 2007). In these mice, in vivo recordings of CA1 neurons demonstrated that place fields were larger and that rate remapping between two environments was impaired in CA3. These observations are consistent with less information being communicated from the DG to these downstream regions in these mice. Finally, it is necessary to revisit the computational models of the hippocampus, DG, and adult neurogenesis. While some models have assumed the pattern separation function and have sought to reassess the mechanism by which the DG network decorrelates its inputs (Myers and Scharfman, 2009), there are other models that have explored other potential roles for the DG. Relevant to this discussion, there have been several models that discuss the DG’s contribution to hippocampal processing as being more sophisticated than simply separating inputs to the hippocampus, such as a proposal that the

DG and hilus form a loop that acts as an and error device to heteroassociations formed in CA3 (Lisman, 1999). Likewise, recent models that have explored the DG’s role in transforming the EC “grid cells” into the place cells common in the CA3 and CA1 may be better understood from a memory resolution view than from a pattern separation perspective (Rennó-Costa et al., 2010). The information content of the DG has been analyzed explicitly in several modeling studies. Indeed, it has been suggested that it is the high-information content of a very few active GCs that is necessary for discrete attractor formation in CA3 (Treves and Rolls, 1992), and this analysis has been extended to show that the low firing rates and sparse connectivity of GCs, when their in vivo spatial behavior (Leutgeb et al., 2007) is considered, is important in determining the information content of place fields in CA3 (Cerasti and Treves, 2010).

The first direct evidence for dendritic dopamine release came from experiments in brain slices directly

measuring release of 3H-dopamine in SN upon potassium-evoked cell depolarization (Geffen et al., 1976). These results were confirmed in vivo using push-pull canula, microdialysis, and voltammetry in the SN and ventral tegmental area (VTA) (Cheramy et al., 1981, Jaffe et al., 1998 and Kalivas and Duffy, 1988). Dopamine is also released from dendrites in more reduced preparations including dissociated culture (Fortin et al., 2006). Interestingly, when VMAT2 is exogenously expressed in dissociated hippocampal neurons, which don’t normally secrete selleck screening library dopamine from their dendrites or axons, robust dendritic dopamine release is observed HKI-272 chemical structure (Li et al., 2005). This experiment reveals that existing activity-dependent secretory pathways in nondopaminergic cells can be co-opted for dopamine release simply by expressing VMAT2, raising the question of what additional release factors might be required for dendritic release from SN dopamine neurons. The mechanism of dendritic dopamine release has been controversial. One study suggested

that dopamine release was not mediated by vesicular fusion, but by reversal of the dopamine transporter (DAT) upon activation of glutamatergic inputs (Falkenburger et al., 2001). However, other studies have shown that DAT inhibitors do not block dopamine release whereas Clostridial neurotoxins that cleave VAMP/synaptobrevin SNARE proteins ( Box 1) block exocytosis of dendritic dopamine, confirming a role for vesicular fusion in dendritic dopamine release ( Bergquist et al., 2002, Fortin et al., 2006 and John et al., 2006). As further evidence for a vesicular mechanism, quantal release events can be recorded from the somatodendritic

region of Rolziracetam dopamine neurons using carbon fiber amperometry ( Jaffe et al., 1998). Although dendritic dopamine appears to be released by vesicle fusion, the mechanisms are distinct from axonal dopamine release. Both dendritic and axonal dopamine release are dependent on Ca2+, but dendritic release is sustained in extracellular Ca2+ levels below those required for axonal release, suggesting different Ca2+ sensors for dendritic and axonal exocytosis (Fortin et al., 2006). The voltage-gated Ca2+ channel coupled to dendritic dopamine release appears to be different from the axonal channel, perhaps explaining the differential release properties of dendrites and axons. Unlike axonal exocytosis, dendritic dopamine release is not blocked by P/Q- or N-type Ca2+ channel blockers (Bergquist et al., 1998, Bergquist and Nissbrandt, 2003, Chen et al., 2006 and Chen and Rice, 2001).

The activation of these receptors can be achieved by intraperitoneal injection of a biologically inert synthetic compound, CNO. There has been concern regarding the potential retro-reduction of CNO to clozapine, a well-known atypical antipsychotic drug (Löffler et al., 2012). However, for several reasons, our results cannot be explained

by this back metabolism. First, while CNO retro-reduction to clozapine has been observed in humans and guinea pigs (Jann et al., 1994), back metabolism AZD6738 research buy could not be detected in rats and mice (Guettier et al., 2009; Jann et al., 1994). Second, CNO-treated GFP-expressing control mice did not show any alteration in any of the tested behaviors compared to saline groups. This includes behavior that has been shown to be sensitive to clozapine such as locomotor activity (McOmish et al., 2012). Finally, clozapine has been reported to alter MD firing (Lavin and Grace, 1998). While we found a decrease in MD neuronal firing rate in MDhM4D mice injected with CNO (Figures 2B–2D) we did not see any firing rate changes in mice that do not express the hM4D receptor (Figure 2C, inset). AZD2281 A major limitation of clinical studies is that they cannot draw causal relations between observed anatomical/functional deficits and specific symptoms. Using pharmacogenetic tools in combination with in vivo recordings from awake behaving

animals we found that even a subtle decrease L-NAME HCl in MD activity can lead to altered MD-PFC functional connectivity and prefrontal-dependent cognitive deficits. Based on these findings we propose that abnormalities found in the MD of patients with schizophrenia could participate to the pathogenesis of cognitive deficits. All protocols used in the present study were approved by the Institutional Animal Care and Use Committee (IACUC) at Columbia University. Mice were C57/Bl6 males purchased from Jackson Laboratory and housed under a 12 hr, light-dark cycle in a temperature-controlled environment with food and water available ad libitum. For the behavioral experiments (reversal learning and T maze tasks), mice were food restricted

and maintained at 85% of their initial weight. For this, they were limited to 1 hr 30 daily access to food in the home cage. After testing, mice were sacrificed and the expression as well as the location of GFP expression was verified to ensure that we correctly targeted the MD (Figure S5). Mice for which GFP was not visible or in an incorrect location were removed from the final analysis. Clozapine-N-Oxide (CNO) (Sigma) was dissolved in PBS to a final concentration of 0.2 mg/ml. PBS or CNO (2 mg/kg) was administered intraperitoneal to the mice 30 min before behavioral testing or in vivo recording (based on personal observations and Alexander et al. [2009]). For thalamic slice physiology CNO was diluted into 1 μM with artificial cerebrospinal fluid.

, 2006). This is an important

lead since the prefrontal cortex is involved in extinction, a type of learning (Santini et al., 2004), but more research is needed to explore the complex relationship between stress, fear conditioning, extinction, and possible morphological remodeling that may well accompany each of these experiences. The prefrontal cortex, amygdala, and hippocampus are interconnected and influence each other via direct and indirect neural activity (Akirav and Richter-Levin, 1999, Ghashghaei and Barbas, 2002, McDonald, 1987, Mcdonald et al., 1996 and Petrovich et al., 2001). For example, inactivation of the amygdala blocks stress-induced impairment CH5424802 ic50 of hippocampal LTP and spatial memory (Kim et al., 2005) and stimulation of basolateral amygdala enhances dentate gyrus field potentials (Ikegaya et al., 1996), while stimulation of medial prefrontal cortex decreases responsiveness of central amygdala output neurons (Quirk et al., 2003). The processing of emotional memories with contextual information requires amygdala-hippocampal interactions (Phillips and LeDoux, 1992 and Richardson et al., 2004), whereas the prefrontal cortex, with its powerful influence on amygdala activity (Quirk et al., 2003), plays an important role in fear extinction (Milad and Quirk, 2002 and Morgan and LeDoux, 1995). Because

of these interactions, future studies need to address their possible role in the morphological and functional changes FGFR inhibitor produced by single and repeated stress. As reviewed above, pyramidal neurons in mPFC display profound behaviorally induced plasticity (i.e., shrinkage and loss of spines with stress), as well as the capacity to recover from stress (i.e., neuronal resilience). In addition, performance on tasks that require PFC is highly vulnerable to decline with age in humans, nonhuman primates, and rodents (reviewed in (Gallagher and Rapp, 1997), and recent data

from NHPs suggest that age-related decline in cognitive performance reliant on PFC may result from loss of a particular class of axospinous synapses on PFC pyramidal neurons (Dumitriu et al., 2010a). More specifically, the NHP data suggest a model in which of large, stable synapses remain unaffected by age, while thin, highly plastic spines are selectively lost from pyramidal neurons within layer III of mPFC (Dumitriu et al., 2010a). The rat model of chronic stress has proven to be a highly valuable model for the analysis of the potential interactive effects of stress and aging on the vulnerable pyramidal neurons in mPFC. For example, is either the behaviorally induced plasticity, i.e., the response to chronic stress, or the capacity to recover from stress affected by aging? These questions were addressed through exposing young, middle-aged, and aged male rats to stress and recovery followed by detailed morphologic analyses of layer III pyramidal neurons in PL (see Figure 3).