g., Fisher et al., 2008). After cognitive training, SZ-AT

subjects performed significantly better on delayed verbal memory recall (NAB; Stern and White, 2003) compared to baseline (t(15) = 2.70, p = 0.02; Figure 3A), but no such improvement was found for the SZ-CG group (delayed recall: t(13) = 1.08, p = 0.30). After training, accuracy for overall source memory identification of word items in the SZ-AT subjects was significantly correlated with better delayed verbal memory recall, even after controlling for age, education, and IQ (delayed recall: r = 0.68, p = 0.01) (Figure 3A); however, no such association was present at baseline (delayed recall: r = 0.23, p = 0.45). Furthermore, PI3K inhibitor after cognitive training, mPFC signal within the a priori ROI was significantly correlated with verbal memory see more scores at 16 weeks (Figure 3B); however, mPFC signal within the a priori ROI in the SZ-AT subjects at baseline did not correlate with delayed recall at baseline (r = −0.04, p = 0.89). No such associations were found in SZ-CG subjects after the intervention (task performance with delayed recall: r = −0.18, p = 0.53; mPFC signal with delayed recall: r = −0.14, p = 0.64). These data indicate that correlations between verbal memory and reality monitoring performance, and between verbal memory and mPFC signal, are

the result of the computerized cognitive training. After cognitive training, the SZ-AT subjects performed significantly better on a measure of executive functioning (Tower of London task; Keefe et al., 2004) compared to baseline (t(15) = 2.47, p = 0.03), a finding not seen in the SZ-CG subjects (t(13) =

0.15, p = 0.89). In SZ-AT subjects, overall source memory identification of word items after training was significantly correlated with performance on executive functioning, even after controlling for age, education and IQ (r = 0.59, p = 0.03), though this association was not present at baseline (r = 0.29, p = 0.28). However, mPFC signal within the a priori ROI at 16 weeks was not associated with executive functioning at 16 weeks (r = 0.31, p = 0.27). No associations between task performance and executive functioning were seen after the intervention in SZ-CG subjects Resminostat (r = 0.05, p = 0.85). These data indicate that cognitive training induces an improvement in executive function in SZ-AT subjects which is associated with better reality monitoring, but not with greater activation in mPFC. Clinical symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS) which rates each symptom–such as delusions or hallucinations–on a scale of 1 (absent) to 7 (extreme) (Kay et al., 1987). Overall mean symptom ratings were low in this clinically stable group of SZ participants (slightly over 2, mild) at baseline and at 16 weeks (Table 3).

Most cells recorded from direction-preferring domains exhibit directional

selectivity, while those recorded outside direction-preferring domains are mainly not directional selective. For example, five out of six cells in penetration 1 show strong direction selectivity. The preferred directions of these five direction cells (95.3° ± 13.4°) are close to the direction preference of the recording site revealed from optical imaging (82.9°; green arrow in Figure 5C). This indicates a columnar organization of direction-selective neurons in direction-preferring domains. There is also a certain Microtubule Associated inhibitor degree of heterogeneity in the direction-preferring domains. For example, one cell did not show significant direction selectivity (cell

1, Direction Index [DI] = 0.33), while others are strongly (cell 3, DI = 0.99) or weakly (e.g., cell 5, DI = 0.71) directional. In non-direction-preferring domains, we also recorded a few direction-selective cells (e.g., cell 3 in penetration 4). However, direction-selective neurons were very rare in regions outside of the direction-preferring domains. In three cases, we recorded 32 cells from seven direction-preferring domains. Twenty-three (72%) of these were direction selective (p < 0.05, Rayleigh test for circular uniformity). Another 31 cells were recorded from nine locations outside of direction-preferring domains. Only two out of these 31 cells (6.5%) were direction selective (p < 0.05, Rayleigh test; DIs = 0.71 and 0.85, respectively). The distributions of direction selectivity and orientation selectivity of cells inside (black) versus outside (gray) direction-preferring Ku 0059436 domains are plotted in Figures 5D and 5E, respectively. Cells recorded from inside direction-preferring domains (DI, 0.63 ± 0.05, n = 32) have higher direction selectivity than cells recorded outside direction-preferring domains (DI, 0.28 ± 0.03, n = 31; p = 1.01 × 10−6, two-sample Kolmogorov-Smirnov test for equal distributions). In contrast, the orientation selectivity of these two groups of neurons

is not significantly different (p = 0.48, two-sample Kolmogorov-Smirnov test). These observations indicate that V4 directional neurons are concentrated in and direction-preferring domains and provide further support for the directional nature of these domains. In V2, direction-preferring domains tend to overlap with orientation-preferring domains but avoid color-preferring domains (Lu et al., 2010). In V4, orientation and color preference maps tend to segregate spatially (Tanigawa et al., 2010). This spatial segregation has been interpreted to indicate some degree of functional independence, while spatial overlap suggests a greater degree of modal integration. Here, we quantitatively evaluated the spatial relationship between direction-preferring domains and orientation- and color-preferring domains.

8%) had glaucoma in both eyes. Seventeen of all included patients (2.9%) were registered in the administration system of the Habilitation and Assistive Technology Service

only. Median time between last visit and death was 8 months this website (interquartile range 3-16 months). Median age at death was 87 years (range 50-103 years). There were 423 patients in the Data at Diagnosis group (71.5%). In those patients mean age at diagnosis was 74.0 ± 7.9 years, ranging from 46-95 years. Exfoliative glaucoma was found in at least 1 eye in 170 patients (40.2%). Average perimetric MD at diagnosis was −5.59 ± 5.69 dB and −11.83 ± 8.18 dB in the better and the worse eye, respectively. Median VA at time of diagnosis was 0.8 (20/25), ranging from no light perception to 1.00 (20/20), in the perimetrically better eye and 0.8 (20/25), ranging from no light perception to 1.25 (20/16), in the perimetrically http://www.selleckchem.com/products/LBH-589.html worse eye. Untreated mean intraocular pressure (IOP) value in all glaucomatous eyes at time of diagnosis was 27.2 ± 8.8 mm Hg. Numbers of patients with low vision and blindness from glaucoma at the last visit are shown in the Table. At the last visit, 42.2% (250 of 592 patients) of all patients were blind from glaucoma in at least 1 eye and 16.4% in both eyes. Other reasons for unilateral blindness

were age-related macular degeneration (AMD) (26 patients), a combination of cataract and other disease (10 patients), and other causes (32 patients). Seventeen patients were bilaterally blind because of reasons other than glaucoma (16 from AMD, 1 patient from other reason). A

combination of causes for blindness was found in 1 eye of 7 blind patients (Table). There was no statistically significant difference in the frequencies these of visual impairment at the last visit when comparing the Data at Diagnosis group and the Follow-up Only group (Table, P = .260). In patients who developed blindness attributable to glaucoma, the median time with bilateral blindness was 2 years (<1-13) (mean 3.0 ± 3.1). Patients who became bilaterally blind from glaucoma did so at a median age of 86 years (range 66-98; mean 85.7 ± 6.1). Only 13 patients (13.5% of blind patients and 2.2% of all patients) became blind before the age of 80 years. The median duration with diagnosed glaucoma was 12 years (<1-29) (mean 11.2 ± 6.6), and 74.7% (316 of 423 patients) of patients had their glaucoma diagnosis for more than 6 years. The cumulative incidence for blindness in at least 1 eye and bilateral blindness from glaucoma was 26.5% and 5.5%, respectively, at 10 years and 38.1% and 13.5%, respectively, at 20 years after diagnosis (Figure 3, Top left and Bottom left). The corresponding cumulative incidences for blindness caused by other reason were 0.7% and 0.7%, respectively, at 10 years and 2.4% and 2.6%, respectively, at 20 years (Figure 3, Top left and Bottom left). The Kaplan-Meier estimates for blindness in at least 1 eye caused by glaucoma were 33.1% at 10 years and 73.

caninum and T. gondii tissue cysts ( Weiss et al., 1999). For signal amplification, the avidin–biotin complex immunoperoxidase step was performed (DakoCytomation, Denmark) and the slides stained with diaminobenzidine tetrahydrochloride (DAB – DakoCytomation). Counter staining was performed with Harris hematoxylin (10%) and slides were later mounted on coverslips to be read under light microscope (Nikon, Japan). Direct detection was also attempted by the detection of Nc5 locus in the IHC positive samples, using DNA extraction, primer

sets and amplification protocols as previously described ( Furuta RG7420 mw et al., 2007). The need to observe N. caninum in wildlife animals has been pointed out as a possible way to understand some obscure aspects of the parasite’s cycle ( Gondim, 2006). There are indications that the presence of birds in cattle-raising farms could be associated with the increase of seroprevalence and abortions related to N. caninum ( Bartels et al., 1999 and Otranto

et al., 2003). In T. gondii epidemiological chain, birds are considered parasite’s reservoir, since those animals are frequently preyed upon by its definitive hosts, felids ( Elmore http://www.selleckchem.com/products/BMS-754807.html et al., 2010). The same pattern of events may be observed in the relationship between dogs and birds, which may lead to speculations towards if birds may also perform the role of N. caninum reservoirs in nature. Epidemiological studies for these protozoa frequently employ antibody detection to estimate population infection rates. Serological positivity to T. gondii in birds is usually low, which is not compatible with direct detection in different tissues ( Dubey, 2002). Serological analysis by IFAT of samples gathered from wild birds maintained in captivity and free-ranging birds for the presence of antibodies to N. caninum were inconclusive, since specific IgG antibodies to N. caninum were not detected. The absence of detectable levels of specific IgG against N. caninum in birds is not a surprise, since it has been already shown that experimentally infected pigeons and different chicken models present

an abrupt antibody seroconversion, Bay 11-7085 despite a brief detection period ( Furuta et al., 2007 and Mineo et al., 2009). Additionally, the same phenomenon has been described in experimental infections of wild birds with T. gondii ( Mineo et al., 2009 and Vitaliano et al., 2010). The lack of detection of circulating antibodies specific to the parasite in the tested species may be partially attributed to the serological assay employed, which is based on a secondary antibody raised for chickens. Although the assay seems to work properly with some wildlife species, IgG domains of different bird species is variable and might not present the same homology with chicken antibodies, fact that may dampen the serological diagnosis in wild life animals. Unfortunately, it is uncommon to find commercial conjugates specific for wild life animals, which limits applied research focusing those species.

The rAAV-SYP1-miniSOG-Citrine titer was measured by quantitative PCR to be 6.6 × 1013 genome copy (GC)/ml (Salk Vector Core). The rAAV-SYP1-miniSOG-T2A-mCherry titer was estimated to be 2.3 × 1013 GC/ml with Quant-IT picogreen dsDNA dye (Life Technologies). Sindbis BGB324 nmr virus containing the tdTomato transgene is produced as described previously ( Malinow et al., 2010). In brief, BHK cells were electroporated with RNAs transcribed from pSinRep5-tdTomato and DH(26S) plasmids. The media was collected

40 hr later and centrifuged to obtain the concentrated virus. Hippocampal microisland cultures were made by a protocol modified from Bekkers (2005). In brief, a collagen (0.5mg/ml, Affymetrix)/poly-D-lysine (0.1mg/ml) mixture was sprayed onto the glass surface of glass bottom dishes (MatTek) with an atomizer. Hippocampal and cortical neurons were extracted from P2 Sprague-Dawley rat pups with papain digestion and mechanical trituration. Hippocampal neurons were transfected by electroporation (Lonza) and plated

at 1.5–3 × 104 cells per dish. Cortical Epigenetic inhibitor in vivo neurons were plated on poly-D-lysine coated dish and infected with rAAV three days after plating. The procedures of extracting cultured neurons and organotypic slices (below) from rat pups were approved by the UCSD Institutional Animal Care and Use Committee. Cultured hippocampal neurons were placed on an Olympus IX71 microscope with 20× air phase contrast objective for the recording (Olympus). Illumination (9.8 mW/mm2) from a xenon arc lamp (Opti-quip) was filtered through a 480/40 nm filter and reflected to the specimen with a full-reflective no mirror (Chroma). Illumination was controlled with a mechanical shutter (Sutter Instrument). Recordings were performed with an Axopatch 200B patch amplifier, Digidata 1332A digitizer, and pCLAMP 9.2 software (Molecular Devices). EPSCs were evoked with a 2 ms voltage step from −60 mV to 0 mV at 0.2 Hz. Illumination

was initiated after 1.5 min of stable baseline (changes <10%) of EPSC amplitude. One hundred percent response for each cell was the mean EPSC amplitude of the 1 min prior to light illumination and the amplitudes of each EPSC were normalized to this 100% response. Reduction of EPSC amplitudes was measured as the mean amplitudes of 6 EPSCs (25 s) after light illumination. Only cells with series resistance <10 MΩ and changes of series resistance <20% after light illumination were analyzed. The external solution contained 118 mM NaCl, 3 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES, and 20 mM glucose (pH 7.35, 315 mOsm). The intracellular pipette solution contained 110 mM K-gluconate, 30 mM KCl, 5 mM NaCl, 2 mM MgCl2, 0.1 mM CaCl2, 2 mM MgATP, 0.3 mM TrisGTP, and 10 mM HEPES (pH 7.25, 285 mOsm). Cortical neurons were recorded with intracellular solution containing 110 mM Cs methanesulfonate, 30 mM tetraethylammonium chloride, 10 mM EGTA, 10 mM HEPES, 1 mM CaCl2, 1 mM MgCl2, 2 mM Mg-ATP, 0.

Altogether, these data support a model whereby gdnf and NrCAM act together to control the acquisition of a repulsive response to Sema3B, which contributes to guide commissural growth cones across the FP. Additional investigations selleck screening library are required to define the exact contribution of each cue, which could underlie the distinct outcome of their invalidation in mice. Several hypotheses can be drawn. First, apart from regulation of Plexin-A1 levels, additional signaling differences between the two cues might be at play to explain the differences. For example, the prominent stalling observed in context of NrCAM deficiency could reflect a contribution of NrCAM in contact interactions

engaging the growth cone with FP cells, as reported in the chick model ( Stoeckli and Landmesser, 1995). Second, distinct www.selleckchem.com/products/MLN8237.html expression levels and/or distribution profiles of NrCAM and gdnf could concentrate their action at a distinct step of the FP crossing. Likewise,

NrCAM loss could essentially affect commissural axon guidance within the FP where the cue might be highly concentrated, whereas gdnf loss would also affect the turning decision at the FP exit, due to larger range of diffusion. Third, in the NrCAM- and gdnf-deficient embryos, the duration of FP crossing could differ. NrCAM loss could slow down the progression of the growth cone, allowing longer exploration and favoring appropriate turning choices. Conversely, in context of gdnf loss, the progression could be unaffected, favoring turning errors. Finally, a hierarchy between gdnf and NrCAM could exist, with NrCAM being only required for reinforcing the gdnf action very locally within SB-3CT the FP, where the sensitization process is taking place. Whatever the case, our study identifies unexpected cooperation between a cell adhesion molecule

and a neurotrophic factor in the regulation of axon path finding. It also provides evidence supporting that complex interplays between different molecular signaling are crucial for the control of guidance choices at critical steps of axon navigation, such as midline crossing. Finally, Shh was reported in previous work to activate the Sema3B midline signaling (Parra and Zou, 2010). In our neuronal cultures, Shh application failed to confer a Sema3B-induced collapse response of commissural neurons. Our observation that the loss of both gdnf and NrCAM fully recapitulate the spectrum of phenotypes resulting from Sema3B/Plexin-A1 deficiency indicates that gdnf and NrCAM are the major triggers of the repulsive Sema3B midline signaling. Thus, if Shh plays a role in this regulation, then it might not be able to compensate in vivo the lack of NrCAM and/or gdnf, as its expression pattern was not altered by gdnf and NrCAM deficiencies ( Figure 1H, Figure S3D). Genotyping of NrCAM mouse line was performed as described in Sakurai et al. (2001).

The robust reciprocal connectivity with PER provides POR with access to information about individual objects,

and connections with other medial temporal structures also provide links to mnemonic input. Moreover, the PER is anatomically and functionally integrated with the amygdala, which is involved in emotion processing and reward learning (LeDoux, 2000; Pitkänen et al., 2000). The POR also receives very strong input from retrosplenial cortex and appears to rely on this information for contextual learning (Keene and Bucci, 2008; Robinson et al., 2012). Thus, the POR is optimally situated to combine object and pattern information from the PER with incoming contextual and spatial information from C646 ic50 retrosplenial and posterior parietal cortices to form complex representations of

specific environmental FK228 chemical structure contexts. The hippocampus is also implicated in contextual learning, so one question of interest is how the processing of contextual information differs between POR and the hippocampus proper. Results of experimental lesion studies of contextual fear conditioning suggest context is processed differently by hippocampus and POR. For example, posttraining lesions of the hippocampus are ineffective 50-100 days after training (Anagnostaras et al., 1999; Maren et al., 1997). In contrast, posttraining PER or POR lesions are effective even 100 days after training (Burwell et al., 2004). Object-location correlates similar to those described here in POR have been observed in the hippocampus. Komorowski et al. (2009)

reported that hippocampal cells signaled item-context conjunctions in a biconditional discrimination task in which the place determined which of two odor stimuli would be rewarded. In that study, item-location conjunctions developed over time as animals learned to associate items with reward. We have not examined the emergence of object-location conjunctions in the POR, but other work suggests that changes in the spatial layout of local stimuli result in immediate remapping in the POR (Burwell and Hafeman, 2003). The evidence suggests that POR supports online processing of context and provides representations of the current context to the hippocampus for the purposes of associative learning and episodic memory. This is consistent with the idea that the hippocampus Thalidomide is located above the PER and POR in a hierarchy of associativity (Lavenex and Amaral, 2000). We suggest that object information in the POR arrives by the well-documented direct PER to POR pathway. Alternatively, it could be that object information arrives at the POR by an indirect pathway that involves both the PER and the hippocampus. Indeed, the PER and POR each have reciprocal connections with the entorhinal cortex and CA1 of the hippocampus, and both project to the subiculum. This alternative view, however, does not account for the function of the direct PER-POR projections.

It is a far more profound concept than a grandmother cell, for it is not about representation (at least not solely) but concerns the intermediate steps of neural computation. In vision, it is a legacy of Hubel and Wiesel, expanded and

elaborated by J.A. Movshon (e.g., Movshon et al., 1978a and Movshon et al., 1978b) and many others. The concept seems to be holding up to the study of decision making. No high-dimensional dynamical structures needed for assembly—at least not so far. In the next 25 years, the field will tackle problems that encompass various levels of explanation, from molecule to networks of circuits. But in selleck the end, the key mechanisms that underlie cognition are likely to be understood as computations supported by the firing rates of neurons that relate directly to relevant quantities of information, evidence, plans, and the steps along the way. Regarding decision making, we have arrived at a point where the three pillars of choice behavior—accuracy, reaction time, and confidence (Link, 1992 and Vickers, 1979)—are reconciled by a common neural mechanism. It has

taken 25 years to achieve this, and it will take another 25, at least, to achieve the degree of understanding we desire at the level of cells, circuits, and circuit-circuit interaction. It will be worth the effort. If cognition is decision making writ large, then the window on cognition mentioned in the title of this essay may one day be a portal to interventions in diseases that affect the mind. M.N.S. is supported by HHMI, NEI, and HFSP. We thank Helen Brew, Chris Fetsch, Naomi Odean, Daphna Shohamy, Luke Woloszyn, and Shushruth for helpful www.selleckchem.com/products/ch5424802.html feedback.

“
“A shift in the understanding of the cerebellum has taken place over the past 25 years. The majority of the human cerebellum is associated with cerebral networks involved in cognition, which is an astonishing finding given that, until quite recently, the cerebellum was thought to contribute primarily to the planning and execution of movements (Strick et al., 2009, Schmahmann, PAK6 2010 and Leiner, 2010). The focus on motor function arose early in the 19th century following careful observations in animal models of cerebellar damage (Ito, 1984). The cerebellum’s anatomical positioning atop the spinal cord and deficits observed in neurological patients led Charles Sherrington (1906) to refer to the cerebellum as the “head ganglion of the proprioceptive system.” Despite sporadic findings supporting a more general role of the cerebellum in nonmotor functions, often conducted by eminent neurophysiologists (Schmahmann, 1997), the overwhelming emphasis of the literature did not waiver from focus on motor control. The motor emphasis was partly driven by a peculiar feature of cerebrocerebellar circuitry that has prevented traditional anatomical techniques from discovering the cerebellum’s full organizational properties (Figure 1).

Moreover, the FLK1-binding VEGF120 isoform did not promote axon growth or growth cone turning in vitro. These findings suggest that NRP1 controls the behavior

of developing RGC axons independently of its vascular coreceptor FLK1, or indeed FLT1, which also is not expressed by developing RGCs. Future studies might therefore examine if NRP1 in RGC axons signals through its cytoplasmic tail or recruits a coreceptor that is not a classical VEGF receptor. VEGF164 has been hypothesized to regulate axon guidance based on its ability to compete with SEMA3A for NRP1 binding (Carmeliet, 2003). However, we could not identify an Z-VAD-FMK clinical trial essential role for SEMA signaling through NRP1 in optic chiasm development in mice. Accordingly, neither the genetic ablation of SEMA3A, nor the loss of SEMA signaling through NRP1 alone or both neuropilins together, perturbed optic chiasm development. These findings were surprising, because the NRP1 ligand

SEMA3D provides repulsive signals that channel RGC axons into the contralateral optic tract in zebrafish (Seth et al., selleck compound 2006). A possible explanation for the class 3 SEMA requirement in fish, but not mammals, is that fish have an exclusive contralateral projection. It will therefore be interesting to investigate whether VEGF-A signaling at the chiasm midline is conserved in all vertebrates, independently of SEMAs, or if there is a species-dependent specialization with respect to the choice of NRP1 ligand. Interestingly, even Drosophila, a species without a circulatory system, has a VEGF-A Dichloromethane dehalogenase homolog that promotes cell migration ( Traver and Zon, 2002). This raises the possibility that VEGF-A plays evolutionary conserved roles in the nervous system that predates its function in blood vessels. Previous in vitro experiments raised the possibility that a growth-promoting factor for commissural axons is present at the chiasm

midline (Tian et al., 2008). However, the molecular identity of this factor has never been established. The only molecule found previously to promote contralateral RGC axon growth is the cell adhesion molecule NrCAM. However, NrCAM is not the elusive midline cue that promotes commissural axon crossing at the optic chiasm, because it acts as a receptor within RGC axons rather than as a guidance signal at the chiasm midline (Williams et al., 2006). In the vertebrate spinal cord, commissural axons are attracted to the midline by the combined action of the chemoattractants netrin 1 and SHH (Serafini et al., 1996 and Charron et al., 2003). However, neither of these molecules is expressed at the chiasm midline or promotes contralateral RGC axon extension (Deiner and Sretavan, 1999, Marcus et al., 1999, Trousse et al., 2001 and Sánchez-Camacho and Bovolenta, 2008).

0. C(m)=∑n=0N−m−1(xn−1N∑i=0N−1xi)(x(n+m)−1N∑i=0N−1xi). Here, xn represents the spine SEP enrichment values at the nth spine, with N the number of spines for the dendrite, and m, the spine lag. The regression lines for spine enrichment values were used to examine distance-dependent changes. To determine the number of synapses in clusters that would produce the autocorrelation values we obtained, we performed simulations (depicted in Figure S6). The following procedure was conducted GDC-941 to generate dendrites with simulated enrichment values satisfying different cluster distributions. We considered a series of 40 spines per dendritic segment and assigned an initial enrichment value

to each spine that varied randomly from 0 to 1. On top of these values, a cluster of enrichment-potentiated spines was added. Two cluster parameters were varied: cluster size and potentiation value of enrichment. A cluster size was characterized by Gaussian-distributed enrichment potentiation values along a dendrite with SD σ = 0.8, 1.2, and 1.6. Enrichment Selleck Docetaxel potentiation p varied from 2 to 5.5. To simulate a dendrite with σ cluster size and potentiation factor p, a Gaussian distribution with

SD = σ and maximum value p was multiplied by a random number (between 0 and 1) at each spine lag and added at a random location within the initial 40 spine enrichment values. By calculating an autocorrelation coefficient for each dendrite and repeating the same procedure 10,000 times, we derived an average autocorrelation curve for each parameter combination. By fitting the true data with the simulated data, we determined σ and the potentiation factor p (i.e., the number of potentiated synapses) in the cluster. Simulations were carried out using MATLAB (MathWorks). We thank C. Cepko for pCALNL-DsRed (Addgene 13769) and pCAG-ERT2CreERT2 (Addgene 13777), W. Guo for cloning the DNA constructs, D. Bortone for technical advice, and J. Isaacson, T. Komiyama and M. Scanziani for critical comments on the manuscript. This study was supported by National Institutes of Health (to

R.M.) and Elizabeth-Sloan Livingston Fellowship (to H.M.). Cell press “
“During brain development neurons establish highly specific synaptic connections with each other. This process is not only regulated by molecular factors that determine, for example, the formation of connections in specific laminae of brain structures, but also by synaptic activity itself (Cline, 2003, Goodman and Shatz, 1993 and Sanes and Yamagata, 2009). In particular, the fine tuning of synaptic connectivity relies on activity-dependent mechanisms that require spontaneous activity that is generated in developing neuronal networks before an organism receives sensory inputs, as well as—later on—activity, which is evoked by sensory experience (Hua and Smith, 2004, Huberman et al., 2008 and Katz and Shatz, 1996).