Measurements were performed using the NIH Image J software. Values were normalized to values obtained for the control group for each litter. E13 rat dorsal spinal cord explants were dissected and embedded in three-dimensional collagen matrices as described (Charron et al., 2003) and cultured in F12:DMEM (1:1), 10% heat-inactivated horse serum, 40 mM glucose, 2 mM glutaMAX, 100 μg/ml streptomycin sulfate, and 100 U/ml penicillin for 16 hr. Where indicated, Netrin-1 (50 or 100 ng/ml) or VEGF (10, 50, or 100 ng/ml) were added to the medium. Commissural axons

were detected by TAG-1 immunostaining and the total length of axon bundles per explant (for outgrowth) was quantified as described previously (Charron et al., 2003). S3I-201 clinical trial We thank D. Schmucker and L. Moons for helpful advice and discussions, A. McMahon (Harvard University) for providing the Wnt1-Cre mouse line to A.C., A. Nagy for providing the VEGFLacZ mice, D. Anderson for providing the VEGFlox/lox mice, and C. Henderson for providing the Sema3E probe for ISH. The authors also thank N. Dai, M. De Mol, A. Manderveld, B. Vanwetswinkel, K. Peeters, L. Goddé, A. Bouché, P. Vanwesemael, J. Van Dijck, S. Morin, and P.T. Yam for assistance. check details This study was supported by “Long-term structural Methusalem funding by the Flemish Government,” the Fund for Scientific Research-Flemish Government (FWO) (G.0319.07, G.0677.09,

G.02010.07, G.0676.09, 1.5.210.10.N.00 [Krediet aan navorsers]), Concerted Research Activities K.U. Leuven (GOA/2006/11), and the Belgian Science Policy (IUAP-P6/20 and IUAP-P6/30), the Association Française contre les myopathies (AFM), Geneeskundige stichting Koningin Elisabeth, and MND/A grant 70/130. C.R.A. is postdoctoral over fellow of the FWO (1.2.545.09.N.00, V.4.332.10.N.01). C.C. is a fellow of the Flemish Institute for the promotion of scientific research (IWT), Belgium. I.S. is a postdoctoral fellow of the European Union Seventh framework program. C.L. is supported by an EMBO long-term postdoctoral fellowship. A.C. is supported by grants from the “Fondation pour la recherche médicale” (programme

Equipe FRM) and the Agence Nationale de la Recherche (ANR-08-MNPS-030-01). F.C. is a Fonds de la recherche en santé du Québec (FRSQ) Scientist. Work performed in the Charron laboratory was supported by an operating grant from the Canadian Institutes of Health Research (CIHR). “
“Alcohol can diminish feelings of anxiety and stress, boost mood, enhance sociability, and induce sleep. Unfortunately, it has also been classified as the most harmful psychoactive drug we abuse (Nutt et al., 2010). Alcohol abuse is widespread, and alcohol use disorders (AUDs) are a debilitating individual and societal problem, diagnosed in over 76 million people worldwide (WHO, 2004). Genetic predispositions have a strong influence on AUDs.

Ch neurons in the Drosophila adult have been implicated as mechanosensory transducers for

acoustic signals ( Eberl, 1999), and also are presumed to be involved in larval propriosensation and mechanosensation ( Caldwell et al., 2003). To assess larval ch sensory neuron functions in a high throughput manner, we developed an assay for larval vibration sensation. Approximately 100 larvae were placed on a large agar-filled dish located above a loud speaker. We used the Multi-Worm Tracker (MWT) software (http://sourceforge.net) (Swierczek N., Giles A., Rankin C. and Kerr R., unpublished data) to automatically deliver vibration stimuli with the speaker while tracking the entire larval population on the dish. Prior to the onset of vibration larvae engage in normal foraging behavior, mostly crawling straight and occasionally MDV3100 making turns. We found that vibration induces a stopping response, followed by head turning ( Figures 7A and 7A′; Movie S1. Startle Response of a Single Wild-Type Drosophila Larvae to Vibration and Movie S2. Analyses of a Group

of Larvae following a Vibration Cabozantinib concentration Stimulus). Larval head turning in response to vibration is highly reproducible and readily quantifiable using the MWT software ( Figures 7B and 7E). This “startle” reaction to mechanical stimuli may allow the larva to sample its environment and change crawling direction following detection of potentially harmful stimuli. We found that atonal (ato1) mutant larvae, which lack ch neurons ( Jarman et al., 1993), do not exhibit a normal response to vibration. Upon stimulation, they show a small decrease in crawling speed (data not shown) with no head turning ( Figures 7C and

7E). We inhibited synaptic transmission in ch neurons by combining the iav-GAL4 with UAS-TNT (tetanus toxin) and found that iav-TNT larvae, which have inactivated ch neurons, do not show significant most increases in head turning in response to vibration as compared to control larvae that express GFP (iav-GFP) in ch neurons ( Figures S7A, S7B, and S7D). Therefore, ch neurons are a major class of larval sensory neurons involved in sensing vibration, and their proper synaptic input to the CNS is required for inducing normal head turning behavior in response to vibration. In Sema-2bC4 mutant larvae we also observed an abnormal response to vibration. Sema-2bC4 mutant larvae do reduce their speed significantly in response to vibration (data not shown), however they show no head turning ( Figures 7D and 7E), similar to the vibration responses observed in ato1 mutant larvae. These results suggest that defective larval vibration responses observed in the absence of Sema-2b result from ch neurons being unable to establish appropriate sensory afferent connectivity within the CNS ( Figure 6F).

For experiments examining syt-lum uptake, immediately after local perfusion, 2 μM TTX was bath-applied for 10 min to isolate spontaneous neurotransmitter release. Neurons were then live labeled with anti-syt-lum for 5 min at RT and processed for immunocytochemistry as described above. The density and intensity of vglut

particles were calculated for each dendritic segment, and the average value was then used for normalizing vlgut density and intensity in all segments (including the treated area). The proportion of vglut particles with syt-lum particles was also determined in each segment. Statistical differences were assessed by ANOVA and Fisher’s LSD post-hoc tests. We thank Richard Tsien, Mia Lindskog, and Rachel Groth for their helpful comments on the manuscript, as well as Hisashi Umemori and members of the Sutton lab for useful discussions. This work was supported JQ1 nmr by RO1MH085798 from The National Institute of Mental Health (M.A.S.) and a grant Small Molecule Compound Library from the Pew Biomedical Scholars Program (M.A.S.). “
“Since the introduction of Dale’s principle of “one neuron releases one fast neurotransmitter” (Dale, 1935), an increasing number of exceptions to this rule have been found in many parts of the nervous system (Burnstock, 2004, Jo and Schlichter, 1999, Jonas et al., 1998, Li et al., 2004, Nishimaru

et al., 2005, Seal and Edwards, 2006, Tsen et al., 2000 and Wojcik et al., 2006), suggesting that corelease of multiple fast neurotransmitters by a single neuron may represent a Thymidine kinase significant mode of neurotransmission. However, the mechanism, circuitry, and function of coneurotransmission in the CNS are poorly understood in general. In the vertebrate retina, starburst amacrine cells (SACs) synthesize and release two classic fast neurotransmitters of opposite excitability, namely acetylcholine (ACh) and gamma-aminobutyric acid (GABA) (Brecha et al., 1988, Kosaka et al., 1988, O’Malley and Masland, 1989 and Vaney and Young, 1988). These cells exist as two mirror-symmetric populations across the inner plexiform layer (IPL), with the somas of one population (conventional or Off SACs) located in the inner nuclear

layer (INL) and those of the other population (displaced or On SACs) in the ganglion cell layer (GCL). The processes (dendrites) of SACs have a radially symmetric (“starburst”) dendritic morphology and ramify in two narrow substrata of the IPL, where the dendrites of neighboring SACs and direction-selective ganglion cells (DSGCs) cofasiculate to form a dense, honeycomb-shaped meshwork (Famiglietti, 1985, Famiglietti, 1992, Famiglietti, 1983, Tauchi and Masland, 1984 and Vaney, 1984). This meshwork is well organized and experimentally approachable, offering a unique opportunity for understanding the mechanism, circuitry, and function of neurotransmitter corelease. SACs are a key component in the direction-selective circuit (Amthor et al.

, 2006). To ensure that we compared the same labels by genes, we compiled a table where each record contains the following fields: Official Gene Symbol by Nomenclature The Entrez Gene this website ID was used as a key

field for comparison. The gene information was extracted from weekly updated gene information files on NCBI repository (ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/). The filter process is conducted in two main steps. First, comparison is done between our query data set and the filter data. We split the query list in two: potential filtered transcripts and potential dendritic/axonal transcripts. The second step is the assessment of false candidate after filtering. False-negative candidates arise in the filtered list due coexpression of those candidates in different cell types. Such records are identified and rescued by comparing the filtered list to transcripts that are present in hippocampus pyramidal signaling pathway neurons (Sugino et al., 2006) or are identified by in situ methods either conducted by us (71 in situ probes) or by previous studies (Table S14). False-positive candidates arise in the cleaned list due to genes that were detected by 454 but were not present on the microarray chip from the reference studies. Those genes were checked in the Allen Brain Atlas for pyramidal neuron expression in area CAI of

the hippocampus. The genes that were de-enriched in the investigated area were pulled out of the result list and a false-positive rate was determined. The Gene Ontology analysis was conducted using the Bingo Plug-In (v 2.44) for Cytoscape (Maere et al., 2005). The Cytoscape output is a text file with the

following parameters: term id, term name, p value, x (number of genes from the query list annotated to a certain term), X (number of genes from the query list that are annotated to a specific ontology), n (total and number of genes annotated to a certain term by the rat genome database), N (total number of genes annotated to an ontology by the rat genome database). One file was generated per ontology (biological process, molecular function, and cellular component). We calculated cluster frequency, total frequency, fold change, for each term graph level, where: ClusterFrequency=100∗xX TotalFrequency=100∗nN FoldChange=Cluster FrequencyTotal Frequency. Three biographs with the ancestors of all overrepresented terms in the corresponding ontology were built. An application was developed in order to search for the shortest path from each overrepresented term to the root of its graph and assign the distance as the depth level for the term (Dijkstra, 1959). We used an additional custom application to combine the results from the three ontologies in one file. The file was imported to Microsoft Excel in order to obtain one table per query list. The table was sorted by depth level and fold change. All terms in the table are overrepresented with p value less than 0.

This provides a functional account of the physiological finding that the response of a V1 neuron to its preferred input within its receptive field is higher when this input pops out from a background than when the same input is just part of a homogeneous texture (Allman et al., 1985, Knierim and Van Essen, 1992 and Marcus and Van Essen, 2002). Lateral connections (Gilbert

and Wiesel, 1983 and Rockland and Lund, 1983) between JAK drugs V1 neurons, leading to mutual suppression between neurons tuned to similar input features, have been suggested as mediating such contextual dependencies of V1 responses. For example, V1 neurons preferring the same or similar orientations are more likely to suppress each other. This iso-orientation suppression reduces V1 neural responses to a homogeneous texture. Meanwhile, V1 neurons preferring, and thus responding to, the pop-out foreground region escape this iso-orientation suppression, more so when the orientation contrast is higher between the

foreground and background bars, making the foreground region more salient according to the V1 saliency hypothesis (Li, 1999 and Li, 2002). This contextual influence on V1 responses is present whether the animal is Selleck Autophagy inhibitor awake (Knierim and van Essen, 1992) or under anesthesia (Nothdurft et al., 1999), regardless of feedback from V2 (Hupé et al., 2001). This bottom-up nature of saliency is in line with the dissociation between attentional attraction and the awareness of the cue in our psychophysical data. Our study succeeded in linking V1 activities directly with saliency

(in terms of cueing effects). In particular, as the orientation contrast between the foreground bars and the background bars increased, V1 neurons responded more vigorously to foreground found bars. This was seen in our data in the form of a larger C1 amplitude, a stronger BOLD signal, and a stronger attentional cueing effect. Until now, only the behavioral predictions of the V1 saliency hypothesis had been tested. These tests have provided various confirmations of the theory including (1), the attentional attraction of an eye of origin singleton (Zhaoping, 2008), whose unique feature is not represented in any visual cortical area other than V1; (2), the close relationship between the reaction times for finding visual search targets and the properties of feature selectivities of the neurons in V1 (and not in extra-striate cortices) (Koene and Zhaoping, 2007); and (3), the alignment between the reaction times in visual search/segmentation tasks and the saliency predicted by the V1 saliency hypothesis (Zhaoping and May, 2007), but not by traditional saliency models (reviewed by Itti and Koch [2001]).

Voltage excursions of ON and OFF CBCs measured in this way had similar amplitudes but opposite signs (Figure 2H; ON CBCs: 13.1 ± 1 mV, n = 27; OFF CBCs: −12.6 ± 1.6 mV, n = 16, p < 10−7). This was true irrespective of whether waves were detected based on the CBC voltage itself or on simultaneously recorded excitation to learn more RGCs (Figure S2). To explicitly test the concurrence of CBC voltage fluctuations with stage III waves, we compared the probability with which RGC EPSCs coincided with CBC depolarizations (ON) or hyperpolarizations (OFF) in recorded traces to simulations in which the timing of CBC events was randomly shifted. In each case, the coincidence of CBC

and RGC events was significantly higher in the recorded than in the randomized traces (Figure 2I, observed: 71% ± 2%, random 17% ± 1%, n = 39, p < 10−7). Since RGC EPSCs at this age were shown to be largely restricted to waves (Blankenship et al., 2009), it follows that the CBC voltage fluctuations we discover here are as well. Events detected only in RGC or CBC traces most likely reflect waves propagating along paths that included most of the neurites of one but not the other neuron recorded. Thus, ON CBCs excite ON RGCs as they depolarize during the ON phase of stage III waves, whereas OFF CBCs, instead of depolarizing during the OFF Selleck PD332991 phase of waves, hyperpolarize during the ON phase and release glutamate onto OFF RGCs as their

voltage returns to baseline. To probe the mechanisms that hyperpolarize OFF CBCs, we carried out voltage-clamp recordings from these cells. In doing so, we observed large IPSCs in OFF CBCs that coincided with EPSCs in simultaneously recorded ON RGCs (Figures 3A and 3B; PT: 30 ± 98 ms, n = 7). Importantly, the inhibitory inputs to OFF CBCs far outweighed coinciding

excitatory ones (Figures 3C and S4C; ginh/gexc: 7.56 ± Phosphatidylinositol diacylglycerol-lyase 1.43, n = 11). Previous results suggest that glycine and GABA receptors mediate inhibition to OFF CBCs at this age (Schubert et al., 2008). Consistent with this, we found that while strychnine (500 nM) alone was sufficient to suppress most wave-associated OFF CBC hyperpolarizations (Figures 3D and 3E), blockade of both glycinergic and GABAergic transmission (strychnine 500 nM, gabazine 5 μM, TPMPA 50 μM) was needed to depolarize OFF CBCs during stage III waves (Figures 3D and 3E; control: −13.8 ± 2.1 mV; −Gly: −0.2 ± 3.1 mV; −Gly −GABAA/C: 7.0 ± 2.7 mV, n = 6; p < 0.03 for all comparisons). Blockade of inhibition had no effect on the amplitude of voltage fluctuations in ON CBCs (control: 16.1 ± 2.9 mV; −Gly −GABAA/C: 15.5 ± 4.3 mV, n = 5; p > 0.8), but raised the frequency of waves in both ON and OFF CBCs (Figure S3; control: 0.082 ± 0.008 Hz; −Gly −GABAA/C: 0.238 ± 0.032 Hz, n = 11, p < 10−3). From these results, we conclude that ON CBCs drive crossover inhibition onto both OFF RGC dendrites and OFF CBC axon terminals.

8% ± 3.5%, n = 14; Cpx KD 78.9% ± 2.5%, n = 14). We also examined whether Cpx KD might affect the proportion of REs containing AMPARs. However, Cpx KD did not affect the percentage of REs containing GluA1 (Figure S3) or the percentage of dendritic GluA1 puncta that colocalized Dasatinib with REs (Figure S3). Cpx KD also did not affect the subcellular localization of REs relative to dendritic spines as defined by simultaneous expression of recombinant TfR fused to mCherry and soluble GFP (Figure S3). Thus, consistent with the lack of

effects of Cpx KD on basal synaptic transmission, these results demonstrate that Cpx KD had no detectable effects on the pool of intracellular AMPARs that are thought to be the source of the AMPARs that are exocytosed during LTP. A final possibility is that the Cpx KD did affect constitutive delivery of AMPARs to synapses but that basal surface expression of AMPARs and thus basal AMPAR EPSCs were not affected because of a compensatory change in the rate of steady state AMPAR endocytosis. To address this possibility, we measured the effect of Cpx KD on constitutive AMPAR endocytosis (Bhattacharyya et al., 2009). There was no detectable effect of Cpx KD on constitutive endocytosis of endogenous surface AMPARs (Figure S3), thus ruling out this hypothesis. Previous work showed

that postsynaptic SNARE-mediated membrane fusion is required for LTP (Kennedy et al., 2010, Lledo et al., 1998 and Lu et al., 2001). However, these experiments focused on SNARE proteins that are ubiquitously involved in both regulated and constitutive membrane fusion Androgen Receptor Antagonist in vitro events. Thus, the mechanisms underlying the regulated, calcium-dependent triggering of AMPAR exocytosis during LTP remained unknown. Using in vivo injection of lentiviruses, we molecularly manipulated complexin only in CA1 pyramidal isothipendyl cells and thus only in the postsynaptic compartment of the excitatory synapses being studied in acute hippocampal slices. The results, which

were confirmed in a neuronal culture model of LTP, provide strong evidence that complexin is a key component of the molecular mechanism by which NMDAR-mediated increases in calcium during LTP induction leads to the exocytosis of AMPARs at the postsynaptic membrane. The importance of postsynaptic complexin in LTP is consistent with immunohistochemical and electron microscopic studies that confirm the presence of complexin in dendritic spines and shafts (McMahon et al., 1995 and Yamada et al., 1999). Our results also suggest that postsynaptic complexin is not required for constitutive delivery of AMPARs and NMDARs into the synaptic plasma membrane, a process that probably occurs on a much slower timescale than the delivery of AMPARs during LTP (Adesnik et al., 2005 and Washbourne et al., 2002). Consistent with this conclusion, Cpx KD had no effects on the intracellular pools of AMPARs found in dendrites or on dendritic REs that have been suggested to be the source of the AMPARs that are exocytosed during LTP (Park et al.

, 2013 and Rudy et al., 2011). The expression, function, and regulation of cortical Htr4 receptors

are clearly different. Htr4 receptors are G-protein coupled, and their expression is strongly and specifically increased in corticostriatal pyramidal cells as a result click here selective serotonin reuptake inhibitor (SSRI) treatment. This has led to the hypothesis that increased Htr4 expression heightens the sensitivity of corticostriatal pyramidal cells to SSRIs, thus improving communication between the cortex and the striatum and contributing to the therapeutic actions of these antidepressants (Schmidt et al., 2012). These two examples of cortical serotonin responses involve different receptors, signaling pathways, cell types, and behavioral outcomes, yet they are elicited by the same neuromodulator. This GW3965 price suggests that any given neuromodulator has the possibility for a wide scope of action. For example, acetylcholine within the cortex has been shown to mediate attention (Froemke et al., 2007) and memory control (Hasselmo, 2006) as well as plasticity (Gil et al., 1997). However, the nucleus basalis is the primary source of acetylcholine to the cortex (Kilgard and Merzenich, 1998), raising the question of

how signaling from a centralized source can mediate such disparate actions. Again, the answer lies in the fact that the receptor families for many modulatory substances are also scattered across distinct cell types and, conversely, that receptors with different signaling capacities

can be coexpressed in the same cell type(s). For instance, both CYTH4 the neurogliaform and VIP-expressing interneurons express nicotinic acetylcholine (ACH) receptors (Lee et al., 2010) in addition to having Htr3a receptors. Other interneuron classes, such as the Martinotti (Kawaguchi and Shindou, 1998) and basket cells (Kruglikov and Rudy, 2008) as well as pyramidal cells, express muscarinic ACH receptors (McGehee, 2002). Hence, the release of acetylcholine can differentially engage and modulate distinct sets of cortical circuits. For instance, recent studies show that VIP-expressing bipolar cells function in the disinhibition of basket and Martinotti cells in fear association (Letzkus et al., 2011) or motor-sensory gating (Lee et al., 2013), respectively. The ability of these cells to increase their gain in response to ACH may begin to explain how they are effective in associating sensory and motor stimuli to behavioral associations. These are just a few of the myriad of possible recruitment strategies at the brain’s disposal.

While we think that much of the sound information is distributed globally,

it is remarkable that a good prediction of the categorization behavior and of its variability could even be obtained with well-chosen single local populations (Figure 8). This indicates that the perceptual decisions made by the animal can be parsimoniously explained by the selection of a relatively small group of neurons that spontaneously provides a suitable categorization of sound stimuli. Such a model would be Autophagy inhibitor in vivo distinct from scenarios in which learning leads to an optimal adaptation of a plastic decision boundary to a continuous sensory representation. What may be the functional role of discrete dynamics in circuits of the auditory cortex? In the primate visual system, complex category signals (objects or groups of

objects) are classically reported in higher areas such as inferior temporal, parietal, and prefrontal areas (DiCarlo et al., 2012; Swaminathan and Freedman, buy GW-572016 2012). The observation of category-forming dynamics already in a primary sensory area suggests that this may be a general property of neocortical circuits. It is conceivable that higher-order categories are built on a hierarchy of lower-order categories which arise in primary sensory areas. Such a hierarchical structure of discrete representations might be essential for elaborate cognitive functions such as language processing. The fact that, e.g., phonemes are perceived and thereby stably recognized as discrete sound categories (Liberman et al., 1967) might rely on similar dynamics of the human auditory cortex. Experimental subjects were male CB57BL/6J mice (Charles River, age: 6 to 16 weeks) and were

performed in accordance with the Austrian laboratory animal law guidelines and approved by the Viennese Magistratsabteilung MA58 (Approval #: M58/02182/2007/11; M58/02063/2008/8). All sounds were delivered free field at 192 kHz sampling rate in a sound proof booth by a custom-made system consisting of a linear amplifier and a ribbon loudspeaker placed nearly 25 cm from the mouse head (Audiocomm, Vienna, Austria). The transfer function between the loudspeaker and the location of the mouse hear was measured using a probe microphone (4939-L-002, Brüel&Kjær, Bremen, Germany) and compensated numerically by filtering the sound files with the inverse transfer function to obtain a flat frequency response at the mouse ear (between 0.5 kHz and 64 kHz ± 4 dB). Sound control and equalization was performed by a custom Matlab program running on a standard personal computer equipped with a Lynx 22 sound card (Lynx Studio Technology, Inc, Costa Mesa, CA).

With these manipulations, the authors demonstrated that apoptosis is further potentiated during the postprandial period if the OB does not receive olfactory inputs, and is blocked by selective sleep deprivation after feeding. To balance this massive cell death, the authors also observed that local sensory deprivation promotes local recruitment of neuroblasts. They concluded that olfactory experience protects cells from death induced by postprandial sleep. Moreover, to disambiguate the role of food intake in this sensory input-dependent cell elimination, Adriamycin cell line the authors showed that apoptosis still increases in mice

entrained to the restricted food paradigm when no food is given but still

experience subsequent sleeping behavior. Therefore, a combination of olfactory experience and subsequent sleeping behavior mediates profound reorganization of OB networks within an hour after feeding. Adult-generated OB neurons are continually turned over, rather than simply added, and the precise balance between new and mature neurons is set through active elimination processes during a critical window. Previous studies have clearly demonstrated that odor learning in an associative task, but not simple exposure to an odor, can efficiently promote newborn cell survival within a critical period (14 to 35 days) after cell birth, while immature (7- Astemizole to 13-day-old) and older cells are not affected (Mouret et al., 2008). Yokoyama and colleagues (2011) now describe a similar critical window for newborn neuron apoptosis in the context of postprandial 3-MA price sleep. This critical period corresponds to a maturation state when newborn cells first receive direct sensory inputs from principal cells of the OB and also top-down inputs from cortical regions such as olfactory cortex. These similar observations may encourage further

studies to establish whether olfactory learning-induced cell survival is related to the postprandial cell elimination reported by Yokoyama et al. (2011). Bearing in mind the relationship between sleep and learning (Maquet, 2001), finding a correlation between the two phenomena should not come as a surprise. Interestingly, the comparison between both behavioral contexts highlights the fact that isolated sensory input alone has no effect. It is only when sensory experience is associated with learning or with postprandrial sleep, two processes that involve top-down inputs to the OB, that it can affect apoptosis. Thus, by detecting the coincidence of sensory and top-down inputs, newborn neurons are ideally positioned to support long-term of memory processes (Lazarini and Lledo, 2011). One recent work has addressed the question of the biological significance of apoptosis in adult OB circuits (Mouret et al., 2009).