8 ± 0.7-fold) and decreased significantly in the cytoplasm (by 60.4% ± 6.2%) in differentiating NPCs (Figure 3C). Therefore, these findings indicate that Axin accumulates in the nuclei of NPCs in response to differentiation signals. The nucleocytoplasmic shuttling of Axin is tightly controlled by the nuclear localization signal (NLS) and nuclear export signal (NES) of the protein (Cong and Varmus, 2004). To elucidate the specific roles of cytoplasmic and nuclear Axin, we generated two point mutants of Axin, allowing the protein to be expressed specifically in the cytoplasm (Axin-NLSm) or nucleus (Axin-NESm) (Figure 3D). Like wild-type Axin, the overexpression

of cytoplasmic Axin (Axin-NLSm) at E13.5 increased ZD1839 mouse the proportion of GFP+ cells in the VZ/SVZ at E15.5 (Figures 3E and 3F), suggesting that cytoplasmic Axin enhances NPC expansion. Furthermore, the re-expression of Axin-NLSm in Axin-knockdown NPCs also led to NPC click here pool expansion (Figures 3G–3L) specifically through the enlargement of the IP population (Figures 3H, 3J, and 3L). In contrast, the expression of nuclear Axin (Axin-NESm) (Figures 3E and 3F) or re-expression of the protein in Axin-knockdown NPCs depleted the GFP+ NPCs in the VZ/SVZ and promoted the differentiation of NPCs into neurons (Figures 3G–3L). Together with the nuclear accumulation of Axin in cultured NPCs upon differentiation (Figures 3A–3C), these findings strongly suggest

Parvulin that Axin in different subcellular compartments of NPCs specifically regulates the amplification and differentiation of NPCs; cytoplasmic Axin in RGs enhances IP amplification,

whereas Axin in the nucleus of IPs promotes neuronal differentiation of IPs. Next, we investigated the molecular mechanism that controls the trafficking of Axin between the cytoplasm and nucleus. Treating RGs with leptomycin B led to the nuclear accumulation of Axin (Cong and Varmus, 2004) (Figure S4A), suggesting that the nuclear enrichment of Axin is regulated by nuclear export. It was noted that the Cdk5-dependent phosphorylation site (Thr485) is located close to the NES of Axin (amino acids 413–423) (Fang et al., 2011) (Figure 4A). Although Axin phosphorylation at Thr485 (p-Axin) could be detected in wild-type mouse neocortices at E13.5, this specific phosphorylation was markedly reduced in cdk5−/− littermates (by 45.5% ± 4.3%; Figures 4B, S4B, and S4C), indicating that Cdk5 is a major kinase that phosphorylates Axin during neurogenesis in vivo. Importantly, the nuclear level of Axin was reduced in cdk5−/− neocortices (by 68.2% ± 5.1%) accompanied by an increased level of cytoplasmic Axin (2.0 ± 0.3-fold; Figure 4B). These results suggest that Cdk5-dependent Axin phosphorylation is critical for controlling the nuclear localization of Axin in the embryonic cerebral cortex. To explore the role of Cdk5-mediated Axin phosphorylation, we examined how Axin phosphorylation is regulated in NPCs.

We simulated a population of correlated, noisy MT neurons with 60 preferred speeds that uniformly tiled the log space between 0.5 and 512 deg/s and 60 preferred directions evenly distributed between −180 and 180 degrees. Each model unit took on a scalar mean response Rmean determined by the sum of the baseline activity R0 and the product of the direction and speed tuning curves: equation(Equation 17) Rmean(θ,s)=R0+ge−0.5(log(s/ps)σs)2e−0.5(θ−pθσθ)2 Here, s and θ are stimulus speed and direction, and ps and pθ are preferred speed and direction. We set amplitude g = 4, R0= 1, bandwidth of the speed tuning σs = 1.5, and bandwidth of the direction tuning σθ = 40. The angle θ-pθ ranged from −180 to +180. The

MEK inhibitor magnitude of the MT-pursuit correlations depended strongly

on the value of g, and we selected the value 4 to reflect our expectation of a mean response of 4 spikes in the 40 ms intervals used to analyze our data for MT neurons with preferred speed and direction near the parameters of target motion. We followed the methods of Shadlen et al. (1996) to add to each neuron’s mean response correlated noise drawn from a normal distribution with the variance scaled to the mean response. The expected correlation structure rij between neurons i and Lumacaftor mouse j was: equation(Equation 18) rij=rmaxe−(log(psi/psj)τs)2e−(pθi−pθjτθ)2 We set the peak correlation rmax = 0.18, and the widths of the correlation structure for speed and direction τs = 1.35 and τθ = PR-171 solubility dmso 45. These values are slightly different from those suggested by our prior report of neuron-neuron correlations in MT ( Huang and Lisberger, 2009). The values were chosen so that the amplitude of the best

model’s MT-pursuit correlations matched those in the data and the neuron-neuron correlations in the model MT populations provided good matches to the data from Huang and Lisberger (2009) for analysis intervals of durations 150 and 300 ms. Given that noise correlations are similar in those two windows, and MT responses are highly correlated across time ( Osborne et al., 2004), we see no reason to think that the noise correlations would be very different in the analysis window of 40 ms used here. We also do not think our conclusions are affected by our assumption that higher-order correlations in the MT population are small and would play little role in the structure of MT-pursuit correlations. We do realize that the exact parameter values for the neuron-neuron correlations are underconstrained by available data, and we take this uncertainty into account in interpreting our results. We thank Mehrdad Jazayeri for insightful comments on an earlier version of the manuscript and Mark Churchland for clarity on issues of population decoding. We are grateful to Lisberger lab members, as well as Jonathan Pillow, Peter Latham, Surya Ganguli, and Valerio Mante for valuable discussions.

Given the rapid LC activation in response to biologically significant events that evoke simple behavioral and autonomic reflexes, it is likely that these phenomena are driven by a common input. In the simplest possible scenario, brainstem nuclei that control autonomic arousal also activate the LC when they are triggered by arousing stimuli (Nieuwenhuis et al., 2010; Pfaff et al., 2012). In that case, LC neurons would simply broadcast the autonomic arousal input to their numerous target regions. But given the direct influence of prefrontal cortices on selleckchem LC neurons, things are probably not that simple (Sara and Hervé-Minvielle, 1995; Jodo et al., 1998). Top-down

influence of prefrontal cortices on both the LC and the autonomic system should modulate their responses in a context-dependent manner. Thus, the implication of the LC in behavioral and cognitive processes probably involves a complex and dynamic interaction of LC with both subcortical structures controlling autonomic arousal and cortical

structures directly involved in attentional and executive functions (Figure 4). Understanding these dynamic interactions is one of the challenges for the future. Steady advances in electrophysiological recording methods over the years since Kupalov first introduced the concept of conditioned cortical arousal have greatly facilitated the study of the relation between behavioral state, arousal, and cognition. New advances within the last decade should accelerate progress in this direction. fMRI BMS-754807 order allows us to observe the primate brain performing razoxane complex cognitive tasks. Continued refinement of methods now enables visualization of tiny nuclei such as LC, although

the temporal resolution is not yet sufficient to capture phasic activation and precise timing of events. On the other hand, rapid development of multichannel, multisite recording and new computing methods give a boost to classical electrophysiological methods for recording from brainstem and cortical ensembles during cognitive activity. Electrophysiological validation of the pupil dilation and other arousal markers as reliable correlates of phasic responses in LC will encourage further research on its role in bistable perception, network reset, and reorienting of attention. These are intriguing hypotheses that await validation. Finally, optogenetics will allow very specific and precise reversible activation and inactivation of the tiny but highly homogeneous noradrenergic nucleus to evaluate impact on cortical activity and cognition (Carter et al., 2010). This review was written while S.J.S. was Visiting Professor at the Institute of Neurosciences, Chinese Academy of Sciences, Shanghai. S.B. is supported from a young investigators grant from the European Research Council. We pay homage to my mentor Corneliu Giurgea (S.J.S.) and to our scientific grandfather (S.J.S.

We believe this first wave of activity is consistent with a combination of intra-area processing and feedforward inter-area processing of the visual image.

The only known means of rapidly conveying information through the ventral pathway is via the spiking activity that travels along axons. Thus, we consider the neuronal representation in a given cortical area (e.g., the “IT representation”) to be the spatiotemporal pattern of spikes produced by the set of pyramidal neurons that project out of that area (e.g., the spiking patterns traveling along the population of axons that project out of IT; see Figure 3B). How is the spiking activity of individual neurons thought to encode visual information? Most studies have investigated the response properties of neurons in the ventral pathway by assuming a firing rate (or, equivalently, a spike CP-868596 concentration LY294002 count) code, i.e., by counting how many spikes each neuron fires over several tens or hundreds of milliseconds following the presentation of a visual image, adjusted for latency (e.g., see Figures 4A and 4B). Historically, this temporal window (here called the “decoding” window) was justified by the observation that its resulting spike rate is typically well modulated by relevant parameters of the presented visual images (such as object identity, position, or size; Desimone et al., 1984, Kobatake and Tanaka, 1994b, Logothetis and Sheinberg,

1996 and Tanaka, 1996) (see examples of IT neuronal responses in Figures 4A–4C), analogous to the well-understood firing click here rate modulation in area V1 by “low level” stimulus properties such as bar orientation (reviewed by Lennie and Movshon, 2005). Like all cortical neurons, neuronal spiking throughout the ventral pathway is variable in the ms-scale timing of spikes, resulting in rate variability for repeated presentations of a nominally identical visual stimulus. This spike timing variability is consistent with a Poisson-like

stochastic spike generation process with an underlying rate determined by each particular image (e.g., Kara et al., 2000 and McAdams and Maunsell, 1999). Despite this variability, one can reliably infer what object, among a set of tested visual objects, was presented from the rates elicited across the IT population (e.g., Abbott et al., 1996, Aggelopoulos and Rolls, 2005, De Baene et al., 2007, Heller et al., 1995, Hung et al., 2005, Li et al., 2009, Op de Beeck et al., 2001 and Rust and DiCarlo, 2010). It remains unknown whether the ms-scale spike variability found in the ventral pathway is “noise” (in that it does not directly help stimulus encoding/decoding) or if it is somehow synchronized over populations of neurons to convey useful, perhaps “multiplexed” information (reviewed by Ermentrout et al., 2008). Empirically, taking into account the fine temporal structure of IT neuronal spiking patterns (e.g.

This implies that GCs represent MC inputs in the

inhibitory current returned to the MCs. As a result, MCs transmit to the cortex errors of GC representations. The responses of GCs in our model are highly nonlinear, with most of them remaining silent. Because MCs play the role of error neurons, their sustained responses are sparse, which is a form of orthogonalization that is alternative to Wick et al., 2010. Overlap reduction in the olfactory bulb network was previously proposed theoretically on the basis of a periglomerular network implementing surround inhibition (Linster and Hasselmo, 1997). This hypothesis was supported by enhanced generalization between chemically similar odorants by rats with strengthened periglomerular inhibition (Linster et al., 2001). We suggest a mechanism for redundancy reduction by GC inhibition that is organized Epacadostat concentration functionally rather than spatially in a task-dependent manner. This check details proposal is consistent with nonlocal interglomerular connectivity (Fantana et al., 2008). The sparseness of the MC responses depends on the nonlinearity of the GCs and, specifically, on the GC activation threshold θ. In this study, we assumed that all GCs have similar activation thresholds that are small enough for GCs to be easily activated by low levels of activity in MCs. If the thresholds for activation of individual GCs are different, it is possible to envision

a mechanism by which the olfactory code carried by both MCs and GCs can be controlled to adapt to a particular task. Thus, if the threshold for activation is raised for a subset of GCs, these cells will be no longer active; therefore, their activity will not be extracted from the firing of MCs. If, for example, the threshold for all of the GCs is increased, thus making them unresponsive, then the olfactory code carried by the MCs replicates their inputs from receptor neurons. If the activation threshold is lowered for a subset of GCs, these cells will

efficiently extract their activity from the MCs’ responses. Thus, a particular redundancy among similar odorants can be excluded in a task-dependent manner. Therefore, Ramoplanin the thresholds for GC activation may regulate both an overall sparseness of MC responses and the fine structure of the bulbar olfactory code. GC excitability depends on cellular properties but can also be effectively modulated by additional input into these cells. The GCs in the mammalian olfactory bulb are recipients of the efferent projections from the cortex and other brain areas (Davis and Macrides, 1981 and Luskin and Price, 1983). These signals to GCs can change their effective threshold values. If a GC receives excitatory inputs from the cortex, then the MC signal is closer to the threshold value, and the GC is more readily excited by the odorant-related inputs.

Our experiments demonstrated a requirement for NMDAr activation for upregulation of BDNF synthesis in tectal neurons, but does not exclude additional roles for other neurotransmitter receptor types. Because postsynaptic depolarization helps relieve the Mg block of NMDArs, AMPAr

activation might also indirectly contribute to enhancing BDNF levels. A direct effect, for example through Ca-permeable AMPARs is also possible, but difficult to learn more test as blocking AMPArs would necessarily also reduce NMDAr currents. Activation of GABA-A receptors could also contribute to this process as the equilibrium potential for Cl may still be depolarizing in some neurons at this developmental stage (Akerman and Cline, 2006). It should be noted that modulation of glutamatergic synaptic transmission by de novo BDNF synthesis, is only one of many elements that contribute to the changes induced by visual conditioning. Diverse protocols using visual stimulation of Xenopus tadpoles have been shown to regulate the expression of Homer 1a, the synthesis of polyamines which modulates ion channel properties, and the activity selleck antibody of small GTPases which regulate cytoskeletal growth ( Aizenman et al., 2002, Sin et al., 2002 and Van Keuren-Jensen and Cline, 2006). However, the unique feature of BDNF we report here is its ability to

bidirectionally facilitate plasticity in its cleaved and uncleaved forms ( Woo et al., 2005). Because of this bidirectional facilitation, experiments that disrupt BDNF signaling are likely to have a more profound effect on refinement compared to manipulations that

modulate plasticity in only one direction. Early sensory activity can influence circuit development both permissively Bumetanide and instructively. Greenough et al. (1987) provided an insightful framework for considering these influences by categorizing developmental plasticity as either “experience-expectant” or “experience-dependent.” The former represents those processes that have evolved to be part of normal development through generations of interactions between the developing brain and a predictable sensory landscape, whereas the latter constitutes a mechanism for adaptation to the different forms of sensory information each unique organism receives. A classic example of experience-dependent plasticity would be the ocular dominance shift observed in response to monocular occlusion. Recent experiments have revealed that while TrkB signaling appears to be dispensable for the deprivation-induced loss of responsiveness to the deprived eye, it is required to mediate the recovery of binocular responses following reopening of the deprived eye (Kaneko et al., 2008).

To test the potential influence of “pause-MLIs” on PCs, we again turned to paired PC recordings and used the large all-or-none CF-PC EPSC as a readout of single CF activation. In a neighboring PC (PC2), we first confirmed the lack of CF or PF

EPSC and then monitored INCB024360 cell line spillover-mediated feedforward inhibition with IPSC recordings (Figures 7A and 7B). PCs receive a high frequency of spontaneous IPSCs that contribute to the signal-averaged inhibition (Konnerth et al., 1990; Figure 7B, middle and bottom) that was unaffected by subthreshold CF stimulation (subthreshold; 110.8% ± 6.4%, n = 24, p > 0.05; Figure 7B). Suprathreshold CF stimulation evoked phasic all-or-none IPSCs in 22 of 46 paired recordings (suprathreshold; Figure 7B) with an onset latency similar to that measured in MLIs (3.9 ± 0.2 ms, n = 22, p > 0.05). Interestingly, suprathreshold CF stimulation also led to the reduction of spontaneous IPSCs, evident in both the individual traces (middle) and the signal-averaged click here responses (bottom traces). Time-locked

and spontaneous IPSCs were quantified by plotting the inhibitory charge (in 5 ms bins) and generating a latency histogram (Figure 7C). CF-evoked all-or-none phasic inhibition was brief (7.2 ± 0.6 ms half-width, n = 22) and resulted in an increase of charge above spontaneous inhibition (583.6% ± 93.3%, n = 22, p < 0.05). After phasic inhibition, CF stimulation reduced the charge of spontaneous IPSCs by 91.5% ± 2.8% (n = 24, p < Linifanib (ABT-869) 0.01), for a duration of 79.9 ± 10.0 ms (half-width, n = 22; Figures 7B and 7Ci). The biphasic change in inhibition persisted in conditions

more similar to those occurring in vivo (1.5 mM extracellular Ca2+ and 37°C, Figure S7; Borst, 2010). TBOA application subsequently increased the evoked inhibition in all nine cell pairs tested, as well as unmasked a CF-evoked IPSC in two additional cell pairs (by 1,115.1% ± 422.9%, n = 11, p < 0.05; and for 14.3 ± 1.8 ms half-width, n = 11). TBOA also prolonged the disinhibition period (115.6 ± 10.8 ms, n = 11, p < 0.05), suggesting that inhibition and disinhibition are generated by CF spillover to MLIs located near and far away from the stimulated CF, respectively (Figures 7B and 7Cii). Supporting this idea, NBQX application blocked both CF-mediated inhibition and disinhibition, demonstrating that feedforward circuits are necessary to engage surrounding PCs (109.9% ± 8.4%, n = 24, p > 0.05; Figures 7B and 7Ciii). Furthermore, AP5 reduced the increase of charge (by 40.6% ± 7.3%, n = 13, p < 0.05) and the quantity and duration of disinhibition (63.5% ± 11.6% and 44.7 ± 14.0 ms, n = 13 for each, p < 0.001 and p < 0.005, respectively; Figure 7Civ), illustrating the prominent role of NMDAR activation after CF-evoked activation of MLIs.

We argue that this was associated—at least partly—with compatible

changes in self-location (mental ball dropping task): a low position Bcl 2 inhibitor or level of self-location (comparable to those indicated during the control conditions; see blue line in Figure 2A) and a drift in self-location characterized by an elevation during synchronous versus asynchronous stroking (difference between the two gray bodies in Figure 2A). This was different in participants from the Down-group. They felt themselves to be looking down at the body below them (different from participants from the Up-group), self-identified with that body during synchronous stimulation (as participants from the Up-group), and experienced themselves to be spatially closer with the virtual body during

synchronous stimulation (as participants from the Up-group). We note that some free reports also suggested that they experienced themselves to be floating and to be elevated during asynchronous stroking. This was associated—at least partly—with compatible changes in self-location (mental ball dropping task): a high position or level of self-location during asynchronous stroking (comparable to those indicated during the control conditions; see blue lines in Figure 2B) and a drift in self-location characterized by a descent during synchronous versus asynchronous stroking (difference between the two gray bodies in Figure 2B that is opposite in direction with respect to the drift-related change in self-location Ruxolitinib manufacturer in the Up-group; black arrows in Figure 2). We next analyzed whether changes in illusory self-location—based on the experimental factors of Stroking, Object, and Perspective—were reflected in the fMRI data. Group-level whole-brain

analysis indicated seven cortical regions where the BOLD signal was significantly different during any of the eight conditions (-)-p-Bromotetramisole Oxalate compared to the baseline condition (Figure 4). These regions (Table S2) were located at the left and right temporo-parietal junction (TPJ), left and right postcentral gyrus (Figures 4A–4C), left and right temporo-occipital cortex (posterior middle and inferior temporal gyri, or extrastriate body area; EBA), and bilateral occipital lobe (Figure 4D). To target brain regions reflecting self-location (as measured by the MBD task; Figure 2) we searched for activity that could not be accounted for by the summation of the effects of seeing the body, feeling synchronous stroking, and the spontaneously reported perspective. Based on our subjective and behavioral data on self-location, we searched for BOLD responses that reflected changes in self-location (i.e., BOLD responses that depend on Stroking and Object), and that also differed for the two perspective groups.

A syp-GFP construct (generated from a mouse complementary DNA clone) was provided by Dr. Niwa (our laboratory). KIF1A and KIF5B expression vectors were generated

by standard molecular methods. Detailed information is provided in the Supplemental Experimental Procedures. Astrocyte cultures were prepared as previously described (Suzuki et al., 2007). The cultures were or were not treated with BDNF (100 ng/ml) for 3 days. Detailed information is provided in the Supplemental Experimental www.selleckchem.com/products/Bortezomib.html Procedures. Neurons were transfected with syp-GFP at 7 DIV and were incubated with or without BDNF (100 ng/ml) for 3 days. At 10 DIV, time-lapse recordings were performed with an LSM710 confocal laser-scanning microscope (Zeiss). We selected the middle part of axons of transfected neurons for live imaging. Images were acquired every 1 s, and syp-GFP containing vesicles moving across the center line of the imaged area were counted. Images were analyzed using ImageJ software. Neurons at 7 DIV were or were not treated with BDNF (100 ng/ml) for 3 days. At 10 DIV, neurons were fixed and immunostained as previously described (Niwa et al., 2008). Cells were fixed with 4% paraformaldehyde in PBS for 10 min, SCH727965 concentration permeabilized

with 0.1% Triton X-100 in PBS, and blocked with 5% bovine serum albumin in PBS. Cells were incubated with primary antibodies overnight at 4°C, followed by incubation with the appropriate Alexa-labeled secondary antibodies for 1 hr. Images were acquired using an LSM510 confocal laser-scanning microscope (Zeiss). Immunopositive puncta

along MAP2-labeled dendrites and synaptophysin/PSD-95-double-positive puncta were counted. For immunocytochemistry, anti-synaptophysin (mouse monoclonal, Chemicon, 1:1000; rabbit monoclonal, Abcam, 1:2000), anti-PSD-95 (mouse monoclonal, ABR, 1:200), and anti-MAP2 (chicken Ribose-5-phosphate isomerase polyclonal, Abcam, 1:2000) antibodies were used. Neurons were transfected with syp-GFP alone or cotransfected with syp-GFP and KIF1A or KIF5B at 7 DIV. At 10 DIV, neurons were fixed and immunostained for MAP2 and PSD-95, and images were acquired as described above. Synaptophysin-GFP puncta along MAP2-labeled dendrites and colocalized with PSD-95 were counted. Data were analyzed by the two-tailed t test or one-way ANOVA with a post hoc Dunnett’s test. For analysis of water maze test data, one-way ANOVA and two-tailed t test were used in the probe test, and two-way repeated-measures ANOVA with a post hoc Bonferroni’s test was used to compare differences between groups at several time points. We thank H. Sato, H. Fukuda, N. Onouchi, T. Akamatsu, T. Aizawa, and all other members of the Hirokawa laboratory for technical assistance and discussions. This work was supported by a grant-in-aid for specially promoted research from the Ministry of Education, Culture, Sports, Science and Technology of Japan (to N.H.).

, 2008) such that viral labeling and expression methods, of the sort used by Carlén et al., would not only have access to ependymal cells, but also to at least some SVZ stem cells. Those caveats aside, the work of Carlén and colleagues nevertheless raises some interesting questions about the impact of Notch signaling on cellular proliferation and the maintenance of specific neural cell types. A recent study in the adult zebrafish brain (Chapouton et al., 2010) has interesting similarities with several of the rodent studies

described above, regarding (1) the role of Notch in stem cell quiescence (Carlén et al., 2009), (2) the coexistence of both proliferatively active and quiescent NSCs in the dentate gyrus (Lugert et al., 2010), and (3) interactions between intermediate progenitors and NSCs (Aguirre et al., 2010). In the zebrafish brain there are radial glial stem cells that can generate new neurons, and CHIR-99021 manufacturer Chapouton et al. found that those radial glia can be either proliferatively active or quiescent and can move back and forth between those states as needed (Chapouton et al., 2010). They argue that the quiescent

state is maintained by Notch signaling, and receptor activation is driven by ligand present on intermediate progenitors. Thus, the more intermediate progenitors there are, the more the system will feed back to activate Notch and inhibit additional NSC divisions. This is similar to the observation made by Carlén et al. in the mouse SVZ that Notch may be required for ependymal cell quiescence (Carlén et al., 2009). SCH727965 molecular weight While some similarities can be noted, the zebrafish study also seems to contradict several mouse studies where Notch receptor or ligand overexpression results in stem cell proliferation and self-renewal rather than quiescence (Aguirre et al.,

2010, Androutsellis-Theotokis et al., 2006, Mizutani et al., 2007 and Yoon et al., 2004). These differences may reveal species-specific phenomena, or may indicate that Notch promotes a cell fate that is quiescent or proliferative, depending upon the availability of other cues. All told, while our understanding of the role of Notch in adult neurogenesis has lagged behind our understanding of it during development, concrete progress Inositol monophosphatase 1 is now underway with numerous studies having emerged recently. Those studies have shown that the fundamentals of Notch signaling during embryonic neurogenesis apply to the germinal zones in the postnatal brain. By studying the well-characterized and highly stereotypical cellular heterogeneity of the postnatal SVZ and SGZ, and how Notch is utilized in distinct subsets of cells, we may uncover novel principles pertinent to Notch regulation in the developing brain as well. A number of studies have examined the role of the Notch pathway in the differentiation of neurons, both during development and postnatally (Berezovska et al., 1999, Breunig et al., 2007, Franklin et al., 1999, Huang et al., 2005, Kurisu et al.